From 5de48f8ffd8dceee0b009cdc474fcccc751b5cbf Mon Sep 17 00:00:00 2001
From: Diego Freitas <diego-xbm@hotmail.com>
Date: Wed, 13 Aug 2025 14:34:22 -0300
Subject: [PATCH] otimizacao do MPC

---
 ...7b53af4-d158-4f90-af96-79b70a65ef93.vsidx} |  Bin 3429733 -> 3429733 bytes
 AgroBase/.vs/AgroBase/v17/.suo                |  Bin 876032 -> 876032 bytes
 AgroBase/AgroBase/Models/Variaveis.cs         |    4 +-
 .../Operadores/HealthWorkerService.cs         |    2 +-
 AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe  |  Bin 6766592 -> 6766592 bytes
 .../EBWebView/Crashpad/settings.dat           |  Bin 280 -> 280 bytes
 .../EBWebView/Default/BrowsingTopicsState     |    2 +-
 .../EBWebView/Default/Cache/Cache_Data/data_0 |  Bin 45056 -> 45056 bytes
 .../EBWebView/Default/Cache/Cache_Data/data_1 |  Bin 270336 -> 270336 bytes
 .../EBWebView/Default/Cache/Cache_Data/data_2 |  Bin 1056768 -> 1056768 bytes
 .../EBWebView/Default/Cache/Cache_Data/data_3 |  Bin 8396800 -> 8396800 bytes
 .../EBWebView/Default/DIPS                    |  Bin 36864 -> 36864 bytes
 .../Default/DawnGraphiteCache/data_1          |  Bin 270336 -> 270336 bytes
 .../EBWebView/Default/DawnWebGPUCache/data_1  |  Bin 270336 -> 270336 bytes
 .../EBWebView/Default/GPUCache/data_1         |  Bin 270336 -> 270336 bytes
 .../EBWebView/Default/History                 |  Bin 360448 -> 360448 bytes
 .../Default/Local Storage/leveldb/LOG         |    6 +-
 .../Default/Local Storage/leveldb/LOG.old     |    6 +-
 .../Default/Network/Network Persistent State  |    2 +-
 .../Default/Network/TransportSecurity         |    2 +-
 .../EBWebView/Default/Preferences             |    2 +-
 .../EBWebView/Default/Session Storage/LOG     |    6 +-
 .../EBWebView/Default/Session Storage/LOG.old |    6 +-
 .../Default/Site Characteristics Database/LOG |    6 +-
 .../Site Characteristics Database/LOG.old     |    6 +-
 .../EBWebView/Default/Web Data                |  Bin 229376 -> 229376 bytes
 .../EBWebView/GrShaderCache/data_1            |  Bin 270336 -> 270336 bytes
 .../EBWebView/GraphiteDawnCache/data_1        |  Bin 270336 -> 270336 bytes
 .../EBWebView/Local State                     |    2 +-
 .../EBWebView/ShaderCache/data_1              |  Bin 270336 -> 270336 bytes
 AgroBase/AgroBase/bin/x64/Debug/AgroBase.pdb  |  Bin 4120064 -> 4120064 bytes
 .../Debug/Parametros/parametersMvd-2_0.par    |    2 +-
 .../bin/x64/Debug/Python/Output/GeoJson.json  |  222 +-
 .../Debug/Python/Output/mapa_interativo.html  |   42 +-
 .../__pycache__/direcional.cpython-311.pyc    |  Bin 18263 -> 19242 bytes
 .../__pycache__/movimentacao.cpython-311.pyc  |  Bin 5222 -> 5375 bytes
 .../modulos/__pycache__/mpc.cpython-311.pyc   |  Bin 95901 -> 100891 bytes
 .../manager_worker/modulos/direcional.py      |   60 +-
 .../manager_worker/modulos/movimentacao.py    |    6 +-
 .../workers/manager_worker/modulos/mpc.py     | 2740 +++++++++--------
 .../processadores/_4_em_andamento.py          |    7 +-
 .../_4_em_andamento.cpython-311.pyc           |  Bin 2435 -> 2460 bytes
 .../__pycache__/padroes.cpython-311.pyc       |  Bin 2910 -> 2964 bytes
 .../manager_worker/processadores/padroes.py   |   17 +-
 .../__pycache__/gps_handler.cpython-311.pyc   |  Bin 6772 -> 7418 bytes
 .../Scripts/workers/shared/gps_handler.py     |    7 +
 .../camera_manager.cpython-311.pyc            |  Bin 70941 -> 92885 bytes
 .../workers/visual_worker/camera_manager.py   |  496 ++-
 Python/OAK/datasets/config.json               |    8 +-
 49 files changed, 2227 insertions(+), 1432 deletions(-)
 rename AgroBase/.vs/AgroBase/FileContentIndex/{da4af7b1-5cf5-47a8-9b35-5f9b77bda5d7.vsidx => 37b53af4-d158-4f90-af96-79b70a65ef93.vsidx} (94%)

diff --git a/AgroBase/.vs/AgroBase/FileContentIndex/da4af7b1-5cf5-47a8-9b35-5f9b77bda5d7.vsidx b/AgroBase/.vs/AgroBase/FileContentIndex/37b53af4-d158-4f90-af96-79b70a65ef93.vsidx
similarity index 94%
rename from AgroBase/.vs/AgroBase/FileContentIndex/da4af7b1-5cf5-47a8-9b35-5f9b77bda5d7.vsidx
rename to AgroBase/.vs/AgroBase/FileContentIndex/37b53af4-d158-4f90-af96-79b70a65ef93.vsidx
index 9da5ad9085fd13827a4ff2999a58e4169280e0d0..565abdf6589dba896a5ecaae6ee08dd35baef081 100644
GIT binary patch
delta 56302
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diff --git a/AgroBase/AgroBase/Models/Variaveis.cs b/AgroBase/AgroBase/Models/Variaveis.cs
index ba1732e6f..30fd087bd 100644
--- a/AgroBase/AgroBase/Models/Variaveis.cs
+++ b/AgroBase/AgroBase/Models/Variaveis.cs
@@ -184,8 +184,8 @@ namespace AgroBase.Models
             { TipoMovimentoDirecional.MovimentoLateral, 90 },
             { TipoMovimentoDirecional.Diagnostico, 25 },
         };
-        public static double AnguloInclinacaoRollMax { get; set; } = 15.0;
-        public static double AnguloInclinacaoPitchMax { get; set; } = 30.0;
+        public static double AnguloInclinacaoRollMax { get; set; } = 30.0;
+        public static double AnguloInclinacaoPitchMax { get; set; } = 15.0;
 
         public static SensorSinaleiroComportamentoModel ComportamentoPorStatus(StatusOperacao? _status = null)
         {
diff --git a/AgroBase/AgroBase/Services/Operadores/HealthWorkerService.cs b/AgroBase/AgroBase/Services/Operadores/HealthWorkerService.cs
index 2c600aee3..3369dcd7f 100644
--- a/AgroBase/AgroBase/Services/Operadores/HealthWorkerService.cs
+++ b/AgroBase/AgroBase/Services/Operadores/HealthWorkerService.cs
@@ -213,7 +213,7 @@ namespace AgroBase.Services.Operadores
                                         distanciaMargem = p.LarguraCorredor * 0.8
                                     })
                                     .ToList(),
-                            horizonte = 2.0,
+                            horizonte = 2.5,
                             angulo_max_graus = _Controle.Angulo_Max,
                             velocidade_min = FuncoesMatematicas.CalculaVelocidadeMsPercentual(_Controle.PercentualVelocidadeMin),
                             velocidade_max = FuncoesMatematicas.CalculaVelocidadeMsPercentual(_Controle.PercentualVelocidadeMax),
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe
index 70d2f6c6db2384a09d93de2fc6d0d7dfdc5e634e..9e03d59dcf3fd8c089b49a9e963170178fa9ca66 100644
GIT binary patch
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zP_(3IMbVm~4Mkgub`<R?0x3FBbfoA+5k%3Mq6<Y=ieQRv6d@GdDMBg2D8eavP()Dl
zr07M_n<A2;4@F;!eiTs@{VAd;22c#77(_9cVhF`hieVJPDMnC?q!>jpn&M@OF&PQI
zW9xLZTWkr*qAkXvdwr~t6z5(aYn@whIC5<5N@1Zb2YnD?2PIZLGu`Tx-(to6$gw>$
a!upIoTx?mSPo)JFqQ+)ah#FUL?f(E#N1uiO

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Crashpad/settings.dat b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Crashpad/settings.dat
index 7d1183dfccdef5ca84955a804ff82b82e83c2647..be17e04813b32142d3b886844fbca8c7b0fe842e 100644
GIT binary patch
delta 62
zcmbQiG=ph^rnX^<OKNDaPikdksAHv<aavYLTA_ZiVOF7Au31r%YjU`@fu~tomSadj
O`oxeu9NU=~zyJV$3=+!#

delta 62
zcmbQiG=ph^rgl_;Ym|jcRcLXAv$Ly-Q$<NhX+=b7RGx)vMS!PsQev^ATR?cQX@GB)
O@5GQj94nX@zyJV}uoF)J

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/BrowsingTopicsState b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/BrowsingTopicsState
index 9b4261054..45a4ba923 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/BrowsingTopicsState
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/BrowsingTopicsState
@@ -29,5 +29,5 @@
       "top_topics_and_observing_domains": [  ]
    } ],
    "hex_encoded_hmac_key": "40F346D3248C3AFDF2BEE1FE496DBD32F7CED6E5AE98B881ABC421AA7E7B5642",
-   "next_scheduled_calculation_time": "13399758321058748"
+   "next_scheduled_calculation_time": "13399758321059072"
 }
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_0 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_0
index fdddc60ef062aceba1ef89a1b16be03dab94c6fe..182dcb0d27ce427a48e551aa5e55b5e24a6db874 100644
GIT binary patch
literal 45056
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delta 4825
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_1
index e8c5953ee25e8236de87f9be43695d72f47fa496..6da0711888631655343bb1ec73868f338cb61662 100644
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_2 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_2
index 48d561c1bab7e3a77764ef57188f11c513e06ee5..89c1df874e1453c7e69980079a88fedc67fa90f3 100644
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delta 865
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_3 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Cache/Cache_Data/data_3
index 279435dea5cc5b8e0576a917bd4edaa7837bb4ec..16ff006826a76cd6a9153282ed8fe693dbf86bef 100644
GIT binary patch
delta 176422
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L5kJIP#&!N5<t`y%

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DIPS b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DIPS
index 1affa90977e8f4cd5c002a4d2fe682799cfb1ab6..f6289feec83da1c2cc307192e6a383b3bf23107c 100644
GIT binary patch
delta 47
zcmZozz|^pSX~PqH869R_wvx=;)S~#D#Nv|pw9KN^6bAiSH<CUD3vF`v&(El{D8K;#
Dsp1jr

delta 47
zcmZozz|^pSX~PqH86{?2wvx=;)S~#D#Nv|pw9KN^6bAiSo9?PlFyG|xpPx}_QGf#g
DrvMSe

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DawnGraphiteCache/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DawnGraphiteCache/data_1
index 708905a67b8ed4c368b639b18eb1c12208da712a..e10a4c72501d465d8b0e713688f503b77601d000 100644
GIT binary patch
delta 32
mcmZoTAkc6?V8aasCiaAhfqKmxitQYVj6lq^okNlN$3FnLw+gBN

delta 32
mcmZoTAkc6?V8aasrcKck1NE9Y6x%rz8G)E-JBK3kkADEi01Hq6

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DawnWebGPUCache/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/DawnWebGPUCache/data_1
index 2442f2fcb77eb03b7562c1ae0420878d3f6b9afb..cc9611cf372ef98eebe490295fee95d630cc590d 100644
GIT binary patch
delta 32
mcmZoTAkc6?V8aasCiaAhfqKmxitQYVj6lq^okNlN$3FnLw+gBN

delta 32
mcmZoTAkc6?V8aasrcKck1NE9Y6x%rz8G)E-JBK3kkADEi01Hq6

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/GPUCache/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/GPUCache/data_1
index 8043256113d4283b88a944a45ced2588ef08b11b..3a909778a37b563426984ddf7df758bb6eb962a9 100644
GIT binary patch
delta 32
mcmZoTAkc6?V8aasCiaAhfqKmxitQYVj6lq^okNlN$3FnLw+gBN

delta 32
mcmZoTAkc6?V8aasrcKck1NE9Y6x%rz8G)E-JBK3kkADEi01Hq6

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/History b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/History
index 1c0fc9e0829ef01b29e1f8d5f7d710ea073dd0d3..b38e9653eb614d2ceea8af83dbe596a1120fbefd 100644
GIT binary patch
delta 2014
zcmd6n|4&tA9LLY|?HusJg<E)KsDQZxtg(TxG%4VwG@)|g=4k8|f_w*wg37cnUKl3}
z#DNTs)2-B2t45oS>bT|DSegD%vXt^Rv&)yucUTtH9JSSRKF@jl8~WjX-ut{iujicS
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zkxXG3DYW7T(GnpA?pjkjk2+YByVm_NUwAV*iV=iJ5yZlyt;2}_u+=(DdfP}h(=E+M
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zgv<(=5i%_#EM!W^q>u?A<3d6ff6*%sqF*ic0{se^=_4f3%xR&?mfAuGG^`xsr~S5h
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z+aVQ*Mu&*wgqK&KF;anO5JY~+=g*(8D|U~h0#WY}pKxN3SD;-|fv9tcmpO5eSD<rJ
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z(jg9T;(5DbH(V+Z`yFB%C${qn^j<0u6+o=d!<?XY#U2BxK$yQhO1vBWx!?Kxxud)S
e6Co9dec~;-CpmG7S70=x0#OEp)ttb3tN#PS0Bx)Q

delta 348
zcmZo@5Nl`<n;^|Nd7_Ln<K&GA_VP^A6ecf}SL8LcG%zqUGdDIfwJ<c>d|sYKfKg%d
z6@6=d{wIu281!dtdi1_Io`Hd(c(R~C{^nl$A_2yGO##~i7+DOMow&QFvo|odZWa{y
z&$Yd~o3T=a*@;JDI{N~!SP%Dhi8YLGn3<jUil?)0U~FMr2~_!<x4n2DBM>ufFW$#I
zv5~oswQoDS1It~e?M%)r%1o0L1lqUvxw4dLF+SMNTEKFcak@+qi`{m%B9>|FOpI|r
zBcd3mFQ{hW*jCNLBFcQ0)o45W1eV#%K#2*A(-pc{Shkx@WjQB>U1lrGIV)y6t~{W_
znQMVsUU6>E`^7SimD!ZtWhy&sHc;pi8?$O_^mNACEaKba*jblxFw1g<O=nMFwgyUy
Va53w*#!U|tW)a(-FU2}B6#%0yYybcN

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG
index a69a96bea..6f7282d52 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG	
@@ -1,3 +1,3 @@
-2025/08/11-17:15:07.450 43b4 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/MANIFEST-000001
-2025/08/11-17:15:07.459 43b4 Recovering log #3
-2025/08/11-17:15:07.463 43b4 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/000003.log 
+2025/08/13-13:54:08.741 7834 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/MANIFEST-000001
+2025/08/13-13:54:08.748 7834 Recovering log #3
+2025/08/13-13:54:08.751 7834 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/000003.log 
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG.old b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG.old
index f6eb62288..cd85d03b9 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG.old	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Local Storage/leveldb/LOG.old	
@@ -1,3 +1,3 @@
-2025/08/11-16:38:48.533 8fe4 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/MANIFEST-000001
-2025/08/11-16:38:48.540 8fe4 Recovering log #3
-2025/08/11-16:38:48.543 8fe4 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/000003.log 
+2025/08/13-13:43:58.156 80a4 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/MANIFEST-000001
+2025/08/13-13:43:58.162 80a4 Recovering log #3
+2025/08/13-13:43:58.165 80a4 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Local Storage\leveldb/000003.log 
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/Network Persistent State b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/Network Persistent State
index aa8ac77c3..30b9ccda3 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/Network Persistent State	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/Network Persistent State	
@@ -1 +1 @@
-{"net":{"http_server_properties":{"servers":[{"alternative_service":[{"advertised_alpns":["h3"],"expiration":"13399499518686465","port":443,"protocol_str":"quic"}],"anonymization":["DAAAAAcAAABmaWxlOi8vAA==",false,0],"network_stats":{"srtt":95712},"server":"https://tile.openstreetmap.org","supports_spdy":true}],"supports_quic":{"address":"192.168.26.32","used_quic":true},"version":5},"network_qualities":{"CAASABiAgICA+P////8B":"4G","CAESABiAgICA+P////8B":"4G","CAISABiAgICA+P////8B":"4G","CAYSABiAgICA+P////8B":"Offline"}}}
\ No newline at end of file
+{"net":{"http_server_properties":{"servers":[{"alternative_service":[{"advertised_alpns":["h3"],"expiration":"13399664052836984","port":443,"protocol_str":"quic"}],"anonymization":["DAAAAAcAAABmaWxlOi8vAA==",false,0],"network_stats":{"srtt":104807},"server":"https://tile.openstreetmap.org","supports_spdy":true}],"supports_quic":{"address":"2804:d78:627:be00:7198:48fb:b727:fd7c","used_quic":true},"version":5},"network_qualities":{"CAASABiAgICA+P////8B":"4G","CAESABiAgICA+P////8B":"4G","CAISABiAgICA+P////8B":"4G","CAYSABiAgICA+P////8B":"Offline"}}}
\ No newline at end of file
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/TransportSecurity b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/TransportSecurity
index eca53d30d..06139dda0 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/TransportSecurity
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Network/TransportSecurity
@@ -1 +1 @@
-{"sts":[{"expiry":1786217642.613677,"host":"bWGAftl61rqoc0YzqPncsLvQQh/iC2Bdp3ejUeGC83w=","mode":"force-https","sts_include_subdomains":true,"sts_observed":1754681642.613684}],"version":2}
\ No newline at end of file
+{"sts":[{"expiry":1786639441.537365,"host":"bWGAftl61rqoc0YzqPncsLvQQh/iC2Bdp3ejUeGC83w=","mode":"force-https","sts_include_subdomains":true,"sts_observed":1755103441.537367}],"version":2}
\ No newline at end of file
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Preferences b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Preferences
index 87a12dc5d..152dcfcd6 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Preferences
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Preferences
@@ -1 +1 @@
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index 152348939..d3af960ad 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG	
@@ -1,3 +1,3 @@
-2025/08/11-17:15:07.352 5f14 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/MANIFEST-000001
-2025/08/11-17:15:07.354 5f14 Recovering log #7
-2025/08/11-17:15:07.354 5f14 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/000007.log 
+2025/08/13-13:54:08.660 9848 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/MANIFEST-000001
+2025/08/13-13:54:08.662 9848 Recovering log #7
+2025/08/13-13:54:08.662 9848 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/000007.log 
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG.old b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG.old
index b2b8d7e21..77f15c28c 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG.old	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Site Characteristics Database/LOG.old	
@@ -1,3 +1,3 @@
-2025/08/11-16:38:48.452 418c Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/MANIFEST-000001
-2025/08/11-16:38:48.453 418c Recovering log #7
-2025/08/11-16:38:48.454 418c Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/000007.log 
+2025/08/13-13:43:58.074 35b4 Reusing MANIFEST C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/MANIFEST-000001
+2025/08/13-13:43:58.076 35b4 Recovering log #7
+2025/08/13-13:43:58.076 35b4 Reusing old log C:\ZendionInc\agrobot_base\AgroBase\AgroBase\bin\x64\Debug\AgroBase.exe.WebView2\EBWebView\Default\Site Characteristics Database/000007.log 
diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Web Data b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Default/Web Data
index 0a08f465c33cbaab83c05409d306e5ac0aa10210..660fd73b91d72d057b7335f937bd0858beb55a07 100644
GIT binary patch
delta 134
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delta 115
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/GrShaderCache/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/GrShaderCache/data_1
index 65f25b8093f6927e0ea771c58069480a3982215b..1b7c668e57db3b0fab39f154c33f1a03d1c04240 100644
GIT binary patch
delta 56
ycmZoTAkc6?V8aasskI^uV6ff^LPtU<hJUUT3&ol_6x%rz8G)E-JBK3kkADD&bq%oq

delta 56
ycmZoTAkc6?V8aassR#iEFsQJF(2)>|;kd)ZLa}BJ#dZ!wMj&R|&Y{Ts;~xNObqwGD

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/GraphiteDawnCache/data_1 b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/GraphiteDawnCache/data_1
index 01adafa53ba4d53eec21a094880a291a92b06863..6147f7a84292ec5921b755d4ca7951be0df24d6b 100644
GIT binary patch
delta 32
mcmZoTAkc6?V8aasCiaAhfqKmxitQYVj6lq^okNlN$3FnLw+gBN

delta 32
mcmZoTAkc6?V8aasrcKck1NE9Y6x%rz8G)E-JBK3kkADEi01Hq6

diff --git a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Local State b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Local State
index 82ff870ad..03fbadedf 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Local State	
+++ b/AgroBase/AgroBase/bin/x64/Debug/AgroBase.exe.WebView2/EBWebView/Local State	
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Parametros/parametersMvd-2_0.par b/AgroBase/AgroBase/bin/x64/Debug/Parametros/parametersMvd-2_0.par
index 3ac291acc..e359bb6e2 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Parametros/parametersMvd-2_0.par
+++ b/AgroBase/AgroBase/bin/x64/Debug/Parametros/parametersMvd-2_0.par
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             );
         
     
-            tile_layer_b64ed6ac3009000233329a32f2decf43.addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+            tile_layer_5d7b162daa8a21dbb285f6d50426847a.addTo(map_e21a72a5cb050d5571718c30559c2190);
         
     
 
@@ -111,7 +111,7 @@
                 }*/
             });
     }
-    function geo_json_92428d205e1566c4dea13c3b7eb4954d_onEachFeature(feature, layer) {
+    function geo_json_27cdeaf4853e626f96b749d12eb65555_onEachFeature(feature, layer) {
 
             layer.on({
             
@@ -148,23 +148,23 @@
         }*/
     });
         };
-        var geo_json_92428d205e1566c4dea13c3b7eb4954d = L.geoJson(null, {
-                onEachFeature: geo_json_92428d205e1566c4dea13c3b7eb4954d_onEachFeature,
+        var geo_json_27cdeaf4853e626f96b749d12eb65555 = L.geoJson(null, {
+                onEachFeature: geo_json_27cdeaf4853e626f96b749d12eb65555_onEachFeature,
             
             ...{
 }
         });
 
-        function geo_json_92428d205e1566c4dea13c3b7eb4954d_add (data) {
-            geo_json_92428d205e1566c4dea13c3b7eb4954d
+        function geo_json_27cdeaf4853e626f96b749d12eb65555_add (data) {
+            geo_json_27cdeaf4853e626f96b749d12eb65555
                 .addData(data);
         }
-            geo_json_92428d205e1566c4dea13c3b7eb4954d_add({"features": [{"geometry": {"coordinates": [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], "id": null, "type": "LineString"}, "id": 0, "properties": {"Dist1": 0.0, "Dist2": 0.0, "Id": "1", "Length": 0.0, "Name": "11_08_2025_16_37_30_Manual"}, "type": "Feature"}], "type": "FeatureCollection"});
-        geo_json_92428d205e1566c4dea13c3b7eb4954d.setStyle(function(feature) {return feature.properties.style;});
+            geo_json_27cdeaf4853e626f96b749d12eb65555_add({"features": [{"geometry": {"coordinates": [[-47.395205344, -22.172559531333334], [-47.395212610166666, -22.172614638833334], [-47.395219157, -22.172656417833334], [-47.395223544833335, -22.1726892105], [-47.39522414098443, -22.17269369161165], [-47.395225326538004, -22.172702654611555]], "id": null, "type": "LineString"}, "id": 0, "properties": {"Dist1": 14.558011415731592, "Dist2": 0.0, "Id": "1", "Length": 14.558011415731592, "Name": "CidadeJardimTerreno2"}, "type": "Feature"}, {"geometry": {"coordinates": [[-47.39521090233333, -22.172708800833334], [-47.395206943666665, -22.172679811], [-47.395202420666664, -22.1726491015], [-47.395198865666664, -22.172615782833333], [-47.395193255833334, -22.172577132833332], [-47.395192610024395, -22.17257265769541], [-47.395191325698136, -22.172563706509337]], "id": null, "type": "LineString"}, "id": 1, "properties": {"Dist1": 14.558011415731592, "Dist2": 0.0, "Id": "2", "Length": 14.771318274761821, "Name": "CidadeJardimTerreno2"}, "type": "Feature"}, {"geometry": {"coordinates": [[-47.3951762925, -22.172561603], [-47.395180824166665, -22.172591686166665], [-47.395185745333336, -22.172623080166666], [-47.395190433, -22.172656367166667], [-47.395195199, -22.172693641833334], [-47.39519576904436, -22.172698125891035], [-47.39519690267159, -22.172707094716973]], "id": null, "type": "LineString"}, "id": 2, "properties": {"Dist1": 14.558011415731592, "Dist2": 0.0, "Id": "3", "Length": 14.827915524386164, "Name": "CidadeJardimTerreno2"}, "type": "Feature"}, {"geometry": {"coordinates": [[-47.39518293216667, -22.1727127985], [-47.39517819266667, -22.172680514833335], [-47.3951732595, -22.172646752833334], [-47.39516880516667, -22.1726149155], [-47.39516381233334, -22.172581167166665], [-47.39516315429932, -22.172576693573966], [-47.39516184565584, -22.172567745443878]], "id": null, "type": "LineString"}, "id": 3, "properties": {"Dist1": 14.558011415731592, "Dist2": 0.0, "Id": "4", "Length": 14.785159385903514, "Name": "CidadeJardimTerreno2"}, "type": "Feature"}, {"geometry": {"coordinates": [[-47.395147317833334, -22.172566031], [-47.395151503166666, -22.172595452333333], [-47.3951561855, -22.172628011166665], [-47.39516159866667, -22.172663757333332], [-47.39516672716667, -22.172697770833334], [-47.395167397572706, -22.172702242832194], [-47.39516873082615, -22.17271118781009]], "id": null, "type": "LineString"}, "id": 4, "properties": {"Dist1": 14.558011415731592, "Dist2": 0.0, "Id": "5", "Length": 14.80117692191233, "Name": "CidadeJardimTerreno2"}, "type": "Feature"}], "type": "FeatureCollection"});
+        geo_json_27cdeaf4853e626f96b749d12eb65555.setStyle(function(feature) {return feature.properties.style;});
 
         
     
-            geo_json_92428d205e1566c4dea13c3b7eb4954d.addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+            geo_json_27cdeaf4853e626f96b749d12eb65555.addTo(map_e21a72a5cb050d5571718c30559c2190);
         
 </script>
 
@@ -185,7 +185,7 @@
         }
         trajeto_json_add({"features": []});
 
-        trajeto_json.addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+        trajeto_json.addTo(map_e21a72a5cb050d5571718c30559c2190);
 
         function adicionarGeometria(novaGeometria) {
             trajeto_json.addData(novaGeometria);
@@ -243,7 +243,7 @@
         }
         trajeto_dinamico_json_add({"features": []});
 
-        trajeto_dinamico_json.addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+        trajeto_dinamico_json.addTo(map_e21a72a5cb050d5571718c30559c2190);
 
         function adicionarGeometriaDinamica(novaGeometria) {
             trajeto_dinamico_json.addData(novaGeometria);
@@ -296,9 +296,9 @@
         
         var marcadorEquipamento = L.marker([0, 0], {
             icon: customIcon
-        }).addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+        }).addTo(map_e21a72a5cb050d5571718c30559c2190);
         
-        var marcadorBase = L.marker([0, 0], {}).addTo(map_b86da2c0cf19440a67c00659ca7d57e9);
+        var marcadorBase = L.marker([0, 0], {}).addTo(map_e21a72a5cb050d5571718c30559c2190);
         var icon = L.AwesomeMarkers.icon(
             {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}
         );
@@ -380,7 +380,7 @@
                 }
                 
                 if (foco) {
-                    map_b86da2c0cf19440a67c00659ca7d57e9.setView(novaPosicao, map_b86da2c0cf19440a67c00659ca7d57e9.getZoom());
+                    map_e21a72a5cb050d5571718c30559c2190.setView(novaPosicao, map_e21a72a5cb050d5571718c30559c2190.getZoom());
                 }
                 
             }
@@ -397,7 +397,7 @@
         function atualizarSelecaoRuas(selecionadas) {
             selecionadas = JSON.parse(selecionadas);
             RuasSelecionadas = Array.isArray(selecionadas) ? [...selecionadas] : [];
-            geo_json_92428d205e1566c4dea13c3b7eb4954d.eachLayer(function (layer) {
+            geo_json_27cdeaf4853e626f96b749d12eb65555.eachLayer(function (layer) {
                 if (RuasSelecionadas.includes(parseInt(layer.feature.id))) {
                     layer.setStyle({ color: 'blue' });
                 } else {
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/__pycache__/direcional.cpython-311.pyc b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/__pycache__/direcional.cpython-311.pyc
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/direcional.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/direcional.py
index 8012748ea..60704c7cd 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/direcional.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/direcional.py
@@ -1,8 +1,12 @@
+import time
+
+import numpy as np
 from shared.enums import ModoOperacao, StatusCarroMapa, StatusModulo, StatusOperacao, T_Code, TipoMovimentoDirecional, TiposControladorDirecional
 from shared.contexto_global_redis import ContextoGlobalRedis, CtxKey
 from manager_worker.config import mostrar_log
 from manager_worker.modulos.mpc import get_mpc, comando_parado
 from manager_worker.modulos.pid import PIDAdaptativo
+from visual_worker.processamento.costmap_fuser import unpack_snapshot
 
 def definir_comando(pid: PIDAdaptativo):
     try:
@@ -11,12 +15,27 @@ def definir_comando(pid: PIDAdaptativo):
 
         _contexto = ContextoGlobalRedis.get_contexto()
         _equipamento = ContextoGlobalRedis.get_equipamento()
-        _gps = ContextoGlobalRedis.get_modulo(T_Code.Gps)
-        _snr = ContextoGlobalRedis.get_modulo(T_Code.Snr)
-        _dados_vw = ContextoGlobalRedis.get(CtxKey.DadosVisualWorker, {})
         _trajetoria = _contexto.get("Trajetoria", {})
+        
+        _snr = ContextoGlobalRedis.get_modulo(T_Code.Snr)
+        visual_worker_ativado = _operacao.get("Snr", {}).get("sonar_ativado", False)
         visual_worker_operante = (_snr.get("saude", {}).get("status", StatusModulo.DESCONECTADO.value)) == StatusModulo.OPERANTE.value
+        _dados_vw = ContextoGlobalRedis.get(CtxKey.DadosVisualWorker, {})
+        _snapshot_vw = _dados_vw.get("matrizes", {}).get("confianca")
+        custo = None
+        nav = None
+        dists = None
+        escalas = None
+        if not _snapshot_vw:
+            visual_worker_atualizado = False
+        else:
+            visual_worker_atualizado = (time.time() - _snapshot_vw.get("ts", 0.0) <= 0.15)
+            if visual_worker_atualizado:
+                custo, conf, anom, nav = unpack_snapshot(_snapshot_vw)
+                dists = _snapshot_vw.get("row_dist_m", None)
+                escalas = _snapshot_vw.get("row_scale_x_m", None)
 
+        _gps = ContextoGlobalRedis.get_modulo(T_Code.Gps)
         _gps_pos_atualizada = _gps.get("heartbeat", 0) != _gps.get("old_heartbeat", 0)
         _gps_passos_travados = 0
         if not _gps_pos_atualizada:
@@ -40,7 +59,7 @@ def definir_comando(pid: PIDAdaptativo):
             },
             "Carro": {
                 #"Velocidade": 1.0, 
-                "Velocidade": max(0.5, _contexto.get("Gerais", {}).get("velocidade_ms", 0.0)), 
+                "Velocidade": max(0.2, _contexto.get("Gerais", {}).get("velocidade_ms", 0.0)), 
                 "Status": _trajetoria.get("status", StatusCarroMapa.Parado.value),
                 "ManobrandoEntreRuas": _trajetoria.get("manobrando", False), 
                 "DentroCorredor": _trajetoria.get("CorredorAtual", {}).get("dentro", False),
@@ -55,17 +74,24 @@ def definir_comando(pid: PIDAdaptativo):
                 "PassosAtraso": _operacao.get("Dir", {}).get("mpc", {}).get("passos_atraso", 1),
             }, 
             "VisualWorker": {
-                "Ativado": _operacao.get("Snr", {}).get("sonar_ativado", False), 
+                "Ativado": visual_worker_ativado, 
                 "Operante": visual_worker_operante, 
+                "Atualizado": visual_worker_atualizado, 
+                "MatrizCusto": {
+                    "Valida": visual_worker_ativado and visual_worker_operante and visual_worker_atualizado, 
+                    "Custo": custo, 
+                    "Navegavel": nav, 
+                    "DistanciasRef": dists, 
+                    "EscalasX": escalas, 
+                },
                 "Camera": {
                     "FovH": ContextoGlobalRedis.get(CtxKey.DadosVisualWorker, {}).get("parametros", {}).get("fov_h", 0.7),
                     "DistanciaMax": _operacao.get("Snr", {}).get("distancia_maxima", 5000) / 1000.0,
                     "DistanciaMin": _operacao.get("Snr", {}).get("distancia_minima", 500) / 1000.0,
-                }, 
-                "MatrizCusto": _dados_vw.get("matrizes", {}).get("custo", [[]]),
+                }
             },
             "RegrasAtivas": {
-                "matriz_custo": False,
+                "matriz_custo": True,
                 "deteccao_obstaculos": False,
             },
             "Equipamento": {
@@ -75,12 +101,14 @@ def definir_comando(pid: PIDAdaptativo):
                 "angulo_pitch_max": _equipamento.get("angulo_pitch_max", 30.0),
             }
         }
-        
+
+        #print(contexto["VisualWorker"])
+
         if _tipo_controle == TiposControladorDirecional.MPC:
             comando = _regras_taticas(contexto)
 
             if (comando["comando_definido"]):
-                return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"]
+                return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"], False
             else:
                 op_modo = ModoOperacao(_operacao.get("modo", ModoOperacao.NaoDefinido.value))
                 if op_modo == ModoOperacao.MapaGPS:
@@ -89,7 +117,7 @@ def definir_comando(pid: PIDAdaptativo):
                     return _comando_mapeamento_visual(contexto)
                 else:
                     comando = _comando_direcional_parado()
-                    return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"]
+                    return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"], False
                 
         elif _tipo_controle == TiposControladorDirecional.PID:
             _controle = ContextoGlobalRedis.get_controle()
@@ -154,8 +182,8 @@ def _regras_taticas(contexto):
 
         imu = ContextoGlobalRedis.get_modulo(T_Code.Imu)
         if imu is not None and imu.get("saude", {}).get("status", StatusModulo.DESCONECTADO.value) == StatusModulo.OPERANTE.value:
-            ang_roll_max = contexto.get("Equipamento", {}).get("angulo_roll_max", 15.0)
-            ang_pitch_max = contexto.get("Equipamento", {}).get("angulo_pitch_max", 30.0)
+            ang_roll_max = contexto.get("Equipamento", {}).get("angulo_roll_max", 30.0)
+            ang_pitch_max = contexto.get("Equipamento", {}).get("angulo_pitch_max", 15.0)
             if abs(imu.get("pitch", 0.0)) > ang_pitch_max or abs(imu.get("roll", 0.0)) > ang_roll_max:
                 mostrar_log(f"🟥 Inclinação perigosa detectada. Parando movimentação. roll: {imu.get('roll')}, pitch: {imu.get('pitch')}, yaw: {imu.get('yaw')}")
                 return _comando_direcional_parado(True)
@@ -203,7 +231,7 @@ def _comando_mapa_gps_mpc(contexto):
 
         #input("⏸️ Pressione Enter para continuar...")
 
-        return comando.get("angulo", 0.0), comando.get("tipo", TipoMovimentoDirecional.RodasDianteiras.value), comando.get("simulacao", []), comando.get("parada_necessaria", False)
+        return comando.get("angulo", 0.0), comando.get("tipo", TipoMovimentoDirecional.RodasDianteiras.value), comando.get("simulacao", []), comando.get("parada_necessaria", False), comando.get("erro", False)
     except Exception as e:
         mostrar_log(f"❌ Erro ao definir comando MapaGPS: {e}")
 
@@ -213,7 +241,7 @@ def _comando_mapeamento_visual(contexto):
         comando = _comando_direcional_parado(True)
         if not contexto.get("VisualWorker", {}).get("Operante", False):
             mostrar_log("Sensores principais inativos! Impossível definir controle")
-            return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"]
+            return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"], False
 
         # 🔍 Dados visuais disponíveis
         _dados_vw = ContextoGlobalRedis.get(CtxKey.DadosVisualWorker, {}).get("segmentacao", {})
@@ -247,7 +275,7 @@ def _comando_mapeamento_visual(contexto):
             "parada_necessaria": parada_necessaria
         }
 
-        return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"]
+        return comando["angulo"], comando["tipo"], comando["simulacao"], comando["parada_necessaria"], False
 
     except Exception as e:
         mostrar_log(f"❌ Erro ao definir comando MapeamentoVisual: {e}")
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/movimentacao.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/movimentacao.py
index f82c5404c..667b73d8a 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/movimentacao.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/movimentacao.py
@@ -3,12 +3,16 @@ from shared.contexto_global_redis import ContextoGlobalRedis, CtxKey
 from manager_worker.config import mostrar_log
 from manager_worker.filtros import FiltroVelocidade
 
-def definir_comando(parada_necessaria: bool, filtro_vel: FiltroVelocidade):
+def definir_comando(parada_necessaria: bool, erro: bool, filtro_vel: FiltroVelocidade):
     try:
         _operacao = ContextoGlobalRedis.get_operacao()
         status_operacao = StatusOperacao(_operacao.get("status", StatusOperacao.NaoIniciado.value))
         finalizando = _operacao.get("finalizando", False)
 
+        if erro:
+            mostrar_log("🟥 Movimento parado: erro ao definir comando direcional.")
+            return 0.0
+
         if parada_necessaria:
             mostrar_log("🟥 Movimento parado: direcional sem possibilidade de desvio.")
             return 0.0
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/mpc.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/mpc.py
index dee6f0981..1007c1281 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/mpc.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/modulos/mpc.py
@@ -1,15 +1,11 @@
+import heapq
 import math
 import time
 import numpy as np
-from copy import deepcopy
-from bisect import bisect_right
-from concurrent.futures import ThreadPoolExecutor, as_completed
 
-from shared.enums import StatusCarroMapa, TipoMovimentoDirecional, TipoPontoRua
+from shared.enums import StatusCarroMapa, TipoMovimentoDirecional
 from manager_worker.config import mostrar_log
-from shared.utils import analisar_linhas_por_profundidade
 from shared.gps_handler import GPSHandler
-#from shared.visualizador_trajetoria import VisualizadorTrajetoria
 
 _mpc = None
 _iniciado = False
@@ -45,1027 +41,362 @@ class ControladorMPC:
             self.horizonte = parametros_mpc.get("horizonte", 2.5)
             self.horizonte_passos = 10
 
-            self.pontos_info = []
-            self.visitados_execucao = []
+            # otimizacoes
+            self._np_bool = np.bool_
+            self._lut_pronto = False
+            self._lut = {}
 
             self.pontos_info = pontos_mapa
+            self.visitados_execucao = np.zeros(len(self.pontos_info), dtype=self._np_bool) if pontos_mapa else []
+            
+            P = np.array([p["xy"] for p in self.pontos_info], dtype=np.float32)
+            M = np.array([p.get("distanciaMargem", 0.7) for p in self.pontos_info], dtype=np.float32)
+            d = np.sqrt(np.sum(np.diff(P, axis=0)**2, axis=1, dtype=np.float32))
+            s = np.concatenate(([0.0], np.cumsum(d)))
+            self._p_xy = P; self._p_margem = M; self._p_s = s
 
-            if pontos_mapa:
-                self.visitados_execucao = [False] * len(self.pontos_info)
+            self._t_est_passo_ms = 6.0   # chute inicial: tempo por expansão (um _simular_passo)
+            self._ema_alpha = 0.2        # suavização da EMA
+            self._mpc_sla_ms = 200.0     # alvo 5 Hz
+            self._mpc_headroom_ms = 25.0 # respiro p/ IO/log/atuadores
+            self.debug_cost_vis = False
 
-            self.executor = ThreadPoolExecutor(max_workers=20)
         except Exception as e:
             mostrar_log(f"Erro ao inicializar MPC: {e}")
 
+    def _ensure_luts(self, dados_matriz_custo, largura_equip, L):
+        if not dados_matriz_custo or dados_matriz_custo.get("Custo") is None:
+            return
 
+        C = dados_matriz_custo["Custo"]
+        escx = dados_matriz_custo.get("EscalasX", [])
+        dist = dados_matriz_custo.get("DistanciasRef", [])
+        dy   = getattr(self, "dy", 0.02)
+
+        base_sig = (
+            C.shape, len(escx), len(dist),
+            round(float(largura_equip), 3),
+            round(float(L), 3),
+            round(float(dy), 4),
+        )
+
+        if getattr(self, "_lut_base_sig", None) != base_sig:
+            # reset do cache base
+            if not hasattr(self, "_lut"):
+                self._lut = {}
+            self._lut_pronto = False               # <- IMPORTANTE
+
+            self._preparar_luts(dados_matriz_custo, largura_equip)
+
+            self._lut_base_sig = base_sig
+            # invalida por-tipo
+            self._lut_tipo = {}
+            self._lut_tipo_sig = {}
+
+    def _ensure_lut_geom_por_tipo(
+        self, dados, largura_equip, tipo,
+        *, L, ang_min, ang_max, passo,
+        k_r=0.5, crab_in_phase=False, beta_crab_deg=4.0
+    ):
+        if not hasattr(self, "_lut_tipo"):
+            self._lut_tipo, self._lut_tipo_sig = {}, {}
+
+        tipo_key = tipo.value
+        sig = (
+            getattr(self, "_lut_base_sig", None),  # vincula ao mapa/escala
+            tipo_key,
+            round(float(L), 3),
+            round(float(largura_equip), 3),
+            float(ang_min), float(ang_max), float(passo),
+            round(float(k_r), 3),
+            bool(crab_in_phase),
+            round(float(beta_crab_deg), 2),
+        )
+
+        if self._lut_tipo_sig.get(tipo_key) == sig:
+            return  # já está pronta
+
+        # (re)constrói LUT geométrica deste tipo (a tua função atual)
+        self._build_lut_geom_por_tipo(
+            dados, tipo,
+            L=L, k_r=k_r, crab_in_phase=crab_in_phase, beta_crab_deg=beta_crab_deg,
+            ang_min=ang_min, ang_max=ang_max, passo=passo
+        )
+        self._lut_tipo_sig[tipo_key] = sig
+
+    def _preparar_luts(self, dados_matriz_custo, largura_equip):
+        if getattr(self, "_lut_pronto", False):
+            return
+
+        # --- distâncias em ordem ASCENDENTE para mapear y -> índice físico ---
+        dist_asc = np.asarray(dados_matriz_custo["DistanciasRef"], dtype=np.float32)  # [H] crescente
+        H = dist_asc.size
+        self.y_min = float(dist_asc.min()); self.y_max = float(dist_asc.max())
+        self.dy = getattr(self, "dy", 0.02)  # mantenha consistente com a assinatura
+
+        # tabela y->índice na MATRIZ (que está em ordem REVERTIDA)
+        n = int(self.y_max / self.dy) + 2
+        lut_y = np.empty(n, dtype=np.int32)
+
+        j = 0  # ponteiro no vetor ascendente
+        for k in range(n):
+            y = k * self.dy
+            while j + 1 < H and dist_asc[j + 1] <= y:
+                j += 1
+            ir = min(j, H - 1)        # índice físico (ascendente)
+            idx = H - 1 - ir          # índice na matriz (reversa)
+            lut_y[k] = idx
+        self._lut["y2idx"] = lut_y
+
+        # --- escalas/offsets por linha (na ordem da MATRIZ: reversa) ---
+        escx_rev = np.asarray(dados_matriz_custo["EscalasX"][::-1], dtype=np.float32)  # [H]
+        W = int(dados_matriz_custo["Custo"].shape[1])
+
+        # footprint em colunas: garanta cobertura central mesmo para colunas pares
+        offsets = np.empty((H, 2), dtype=np.int32)
+        for i, sx in enumerate(escx_rev):
+            cols = int(np.ceil(largura_equip / sx))
+            # centraliza footprint: para cols pares, deixa 1 a mais à direita
+            halfL = cols // 2
+            halfR = cols - 1 - halfL
+            offsets[i] = (-halfL, halfR)
+        self._lut["offsets"]  = offsets
+        self._lut["escala_x"] = escx_rev
+        self._lut["H"] = H
+        self._lut["W"] = W
+        self._lut["dist_asc"] = dist_asc
+        self._lut["dist_rev"] = dist_asc[::-1]
+
+        # NADA de construir LUT por tipo aqui — isso é função do _ensure_lut_geom_por_tipo
+        self._lut_pronto = True
+
+    def _build_lut_geom_por_tipo(self, dados, tipo,
+                             ang_min=-30.0, ang_max=30.0, passo=1.0,
+                             L=1.0, k_r=0.5, crab_in_phase=False, beta_crab_deg=4.0):
+        C = dados["Custo"]; W = int(C.shape[1])
+        escx = np.asarray(dados["EscalasX"][::-1], dtype=np.float32)      # [H]
+        dist = np.asarray(dados["DistanciasRef"][::-1], dtype=np.float32) # [H]
+        offs = self._lut["offsets"]                                       # [H, 2]
+        H = dist.size
+
+        ang_grid_deg = np.arange(ang_min, ang_max + 1e-6, passo, dtype=np.float32)  # [K]
+        ang_grid_rad = np.deg2rad(ang_grid_deg)
+
+        if crab_in_phase:
+            kappa = np.zeros_like(ang_grid_rad)
+            beta  = float(np.deg2rad(beta_crab_deg))
+        else:
+            kappa = np.array([self._kappa_from_angle(tipo, a, L=L, k_r=k_r) for a in ang_grid_rad], dtype=np.float32)
+
+        y = dist[:, None]   # [H,1]
+        k = kappa[None, :]  # [1,K]
+
+        if crab_in_phase:
+            x = y * math.tan(beta)
+        else:
+            ky = k * y
+            x = np.where(
+                np.abs(ky) < 1e-4,
+                0.5 * k * (y**2),
+                (np.sign(k) / np.maximum(np.abs(k), 1e-9)) * (1.0 - np.cos(np.abs(ky)))
+            ).astype(np.float32)
+
+        sx = escx[:, None]      # [H,1]
+        j_c = np.floor((x + (W * sx) * 0.5) / sx).astype(np.int32)
+
+        j0 = np.clip(j_c + offs[:, [0]], 0, W - 1).astype(np.int32)
+        j1 = np.clip(j_c + offs[:, [1]], 0, W - 1).astype(np.int32)
+
+        if not hasattr(self, "_lut_tipo"):
+            self._lut_tipo = {}
+
+        self._lut_tipo[tipo.value] = {
+            "ang_grid_deg": ang_grid_deg,
+            "j0": j0,
+            "j1": j1,
+            "passo": float(passo),
+        }
+
+    def _map_ang_to_idx_fast(self, angulos_deg, lut_tipo):
+        grid0 = float(lut_tipo["ang_grid_deg"][0])
+        step  = float(lut_tipo["passo"])
+        K     = lut_tipo["ang_grid_deg"].size
+        a = np.asarray(angulos_deg, dtype=np.float32)
+        idx = np.floor((a - grid0)/step + 0.5).astype(np.int32)
+        return np.clip(idx, 0, K-1)
+
+    def _kappa_from_angle(self, tipo, a_rad, L, k_r=0.5, crab_in_phase=False, beta_crab=0.0):
+        """
+        Mapeia ângulo de direção 'a_rad' -> curvatura κ (1/m) de acordo com o tipo.
+        - crab_in_phase=True: movimento 'crab' (quase κ=0), usa desloc. lateral beta_crab (rad).
+        """
+        if crab_in_phase:
+            return 0.0  # tratamos crab fora (x ≈ y * tan(beta_crab))
+        if tipo == TipoMovimentoDirecional.RodasDianteiras:
+            return math.tan(a_rad) / L
+        elif tipo == TipoMovimentoDirecional.MovimentoArco:
+            return math.tan((1.0 - k_r) * a_rad) / L
+        else:
+            return math.tan(a_rad) / L  # default
+
+    
     # UTILS
 
     def _proximo_nao_visitado(self, visitados):
-        for i, v in enumerate(visitados):
-            if not v:
-                return i
-        return len(visitados) - 1
+        try:
+            for i, v in enumerate(visitados):
+                if not v:
+                    return i
+            return len(visitados) - 1
+        except Exception as e:
+            mostrar_log(f"Erro ao consultar proximo ponto nao visitado: {e}")
 
-    def _corrigir_pontos_visitados(self, x, y, pontos_visitados, idx_atual=-1, limite_max_avanço=5.0):
-        if (idx_atual > -1):
-            for idx, ponto in enumerate(self.pontos_info):
-                if (idx < idx_atual):
-                    pontos_visitados[idx] = True
-                else:
-                    break
-            return idx_atual
-        
-        for idx, ponto in enumerate(self.pontos_info):
-            if pontos_visitados[idx]:
-                continue
-            pos = ponto["xy"]
-            margem = ponto.get("distanciaMargem", 0.7)
-            dist = np.linalg.norm([x - pos[0], y - pos[1]])
-            if dist < margem:
-                for i in range(idx + 1):
-                    pontos_visitados[i] = True
+    def _corrigir_pontos_visitados(
+        self, x, y, pontos_visitados: np.ndarray, idx_atual: int = -1, 
+        limite_max_avanço: float = 5.0  # em METROS
+    ):
+        """
+        Marca como visitados até o ponto mais recente cuja distância ao (x, y) cai dentro da 'margem' do ponto.
+        Usa janela limitada por distância acumulada (metros) para não varrer toda a lista.
+        """
+        try:
+            # 1) caminho em arrays (pré-calcule isso uma vez no __init__ ou quando pontos mudarem)
+            # self._p_xy : (N,2) float32  | self._p_margem : (N,) float32  | self._p_s : (N,) float32 (dist acumulada)
+            p_xy = self._p_xy       # np.ndarray
+            p_mg = self._p_margem   # np.ndarray
+            p_s  = self._p_s        # np.ndarray (metros)
+
+            N = p_xy.shape[0]
+            if N == 0:
+                return -1
+
+            # 2) fast-path: se idx_atual fornecido, só marca e sai
+            if idx_atual > -1:
+                pontos_visitados[:idx_atual+1] = True
+                return idx_atual
+
+            # 3) comece da "fronteira" atual (primeiro False)
+            # se já tiver tudo visitado, devolve último índice
+            if pontos_visitados.all():
+                return N - 1
+
+            # próximo não visitado (rápido)
+            start_idx = int(np.argmax(~pontos_visitados))
+
+            # 4) limitar varredura por distância acumulada (em METROS)
+            # queremos procurar até "start_idx" + Δs (metros), convertendo via s_acum
+            s_start = p_s[start_idx]
+            s_lim   = s_start + float(limite_max_avanço)
+
+            # encontre o último índice com s <= s_lim (searchsorted é O(log N))
+            # atenção: searchsorted trabalha com array crescente
+            end_idx = int(np.searchsorted(p_s, s_lim, side="right") - 1)
+            if end_idx < start_idx:
+                end_idx = start_idx
+            # também pode pôr um teto de tamanho de janela (evita pegar centenas de pontos de uma vez)
+            # end_idx = min(end_idx, start_idx + 150)
+
+            # 5) checagem vetorizada na janela [start_idx:end_idx] (inclusivo)
+            sl = slice(start_idx, end_idx + 1)
+            dx = x - p_xy[sl, 0]
+            dy = y - p_xy[sl, 1]
+            dist2   = dx*dx + dy*dy
+            margem2 = p_mg[sl] * p_mg[sl]
+
+            hits = dist2 <= margem2
+            if np.any(hits):
+                # pega o primeiro índice da janela que bateu
+                off = int(np.argmax(hits))  # primeiro True
+                idx = start_idx + off
+                # marca até ele (inclusive)
+                pontos_visitados[:idx+1] = True
                 return idx
-            if idx > 0 and not pontos_visitados[idx - 1] and idx >= limite_max_avanço:
-                break
-        return self._proximo_nao_visitado(pontos_visitados)
+
+            # 6) se ninguém bateu dentro da janela, mantém visitados como estão e retorna o próximo não visitado
+            # (isso replica seu comportamento original)
+            prox = int(np.argmax(~pontos_visitados))
+            return prox
+
+        except Exception as e:
+            mostrar_log(f"Erro ao corrigir pontos visitados: {e}")
+            # fallback conservador
+            try:
+                prox = int(np.argmax(~pontos_visitados))
+                return prox
+            except Exception:
+                return -1
 
     def _calcular_pesos_movimento(self, contexto, erro_ori_rad):
-        peso_erro_pos = 1.2  # 0
-        peso_erro_ori = 1.0  # 1
-        peso_suavidade = 1.4 # 2
-        peso_fator_re = 0.2  # 3
-        peso_ideal = 0.4     # 4
-
-        CARRO = contexto.get("Carro", {})
-        status = StatusCarroMapa(CARRO.get("Status", StatusCarroMapa.Parado.value))
-        dentro = CARRO.get("DentroCorredor", False)
-        manobrando = CARRO.get("ManobrandoEntreRuas", False)
-        velocidade = CARRO.get("Velocidade", self.velocidade_min)
-
-        # Penalidade proporcional à velocidade
-        penalidade_por_velocidade = (velocidade / self.velocidade_max) / 10.0
-        erro_ori_deg = np.degrees(erro_ori_rad)
-        erro_ori_clipped = max(min(erro_ori_deg, self.angulo_max_graus), -self.angulo_max_graus)
-        erro_ori_norm = abs(erro_ori_clipped / self.angulo_max_graus)
-
-        custo_movimento = {
-            TipoMovimentoDirecional.RodasDianteiras: 0.0,
-            TipoMovimentoDirecional.MovimentoArco: 0.0
-        }
-
-        if manobrando or status in [ StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando ]:
-            custo_movimento[TipoMovimentoDirecional.MovimentoArco] += 0.0
-            custo_movimento[TipoMovimentoDirecional.RodasDianteiras] += 1.0
-        else:
-            custo_movimento[TipoMovimentoDirecional.MovimentoArco] += 0.0 if erro_ori_norm >= 0.4 else 1.0
-            custo_movimento[TipoMovimentoDirecional.RodasDianteiras] += 1.2 if erro_ori_norm >= 0.4 else 0.0
-
-        return (peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_ideal, custo_movimento)
-
-    def _gerar_angulos_candidatos(self, angulo_ideal_rad, erro_ori, contexto):
-        def clamp(v, vmin, vmax):
-            return max(vmin, min(vmax, v))
-
-        CARRO = contexto.get("Carro", {})
-        status = CARRO.get("Status", 0)
-        manobrando = CARRO.get("ManobrandoEntreRuas", False)
-
-        if manobrando or status in [ StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando ]:
-            tipos_validos = [ TipoMovimentoDirecional.MovimentoArco ]
-        else:
-            tipos_validos = [ TipoMovimentoDirecional.RodasDianteiras, TipoMovimentoDirecional.MovimentoArco ]
-
-        erro_ori_deg = np.degrees(erro_ori)
-        angulo_ideal_deg = np.degrees(angulo_ideal_rad)
-
-        # Passo varia conforme erro angular
-        if erro_ori_deg > 10:
-            passo = 5.0
-            faixa = 15.0
-        elif erro_ori_deg > 5:
-            passo = 2.5
-            faixa = 7.5
-        else:
-            passo = 1.5
-            faixa = 4.5
-
-        # Geração em torno do ideal
-        candidatos_graus = list(np.arange(angulo_ideal_deg - faixa, angulo_ideal_deg + faixa + 0.1, passo))
-
-        extras = [
-            -self.angulo_max_graus,              # mínimo possível
-            -self.angulo_max_graus * 0.75,       # -75%
-            self.angulo_max_graus * 0.75,       # +75%
-            self.angulo_max_graus              # máximo possível
-        ]
-
-        # Clamping e eliminação de duplicatas
-        candidatos_radianos = sorted(set(
-            np.radians(clamp(g, -self.angulo_max_graus, self.angulo_max_graus))
-            for g in (candidatos_graus + extras)
-        ))
-
-        #print(f"Ideal: {round(angulo_ideal_deg, 2)}°, Erro Ori: {round(erro_ori_deg, 2)}°, Obstáculos: {len(obstaculos)}, Candidatos: {[round(np.degrees(a), 2) for a in candidatos_radianos]}")
-        return tipos_validos, candidatos_radianos
-
-    def _erro_angular(self, angulo_alvo, angulo_atual, angulo_caminho, peso_local=0.5):
-        erro_com_ponto = (angulo_alvo - angulo_atual + np.pi) % (2 * np.pi) - np.pi
-        erro_com_caminho = (angulo_caminho - angulo_atual + np.pi) % (2 * np.pi) - np.pi
-
-        erro_combinado = (1 - peso_local) * erro_com_caminho + peso_local * erro_com_ponto
-        return abs(erro_combinado)
-    
-    def _atualiza_posicao_obstaculos(self, contexto, new_x, new_y, new_theta, omega):
-        obstaculos = deepcopy(contexto.get("VisualWorker", {}).get("Obstaculos", []))
-        velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-        for obst in obstaculos:
-            obst["distancia_m"] = max(0.0, obst["distancia_m"] - velocidade * min(0.2, self.dt))
-            # Assume que o obstáculo está a 'distancia_m' à frente, em um ângulo correspondente ao centro_percentual original
-            fov = contexto["VisualWorker"]["Camera"]["FovH"]  # exemplo: np.radians(70)
-            centro_antigo = obst.get("centro_percentual", [0.5, 0.5])[0]
-            angulo_original = (centro_antigo - 0.5) * fov
-            # Estima a posição real do obstáculo
-            x_obst = new_x + obst["distancia_m"] * np.cos(new_theta + angulo_original)
-            y_obst = new_y + obst["distancia_m"] * np.sin(new_theta + angulo_original)
-            # Recalcula o centro_percentual estimado
-            centro_x = centro_antigo - (omega / fov)
-            obst["centro_percentual"] = [centro_x, obst.get("centro_percentual", [0.5, 0.5])[1]]
-            #print(f"📍 (x: {new_x}, y: {new_y}, t: {new_theta}) Obstáculo simulado em x: {x_obst:.2f}, y: {y_obst:.2f}, omega: {np.degrees(omega):.2f}°, centro_x: {obst['centro_percentual'][0]:.3f} ({centro_x:.3f}) / ({centro_antigo:.3f})")
-        return obstaculos
-
-    def _simular_trajetoria_matriz_custo(self, matriz_custo, linhas_info, tipo, angulo, distancia_m, theta_rad, largura_equipamento):
-        altura = len(matriz_custo)
-        largura = len(matriz_custo[0])
-        if largura == 0 or altura == 0 or distancia_m == 0:
-            return 0.0, True
-
-        # 2. Ordena por profundidade crescente
-        linhas_ordenadas = sorted(linhas_info.values(), key=lambda item: item["distancia"])
-        distancia_min_m = linhas_ordenadas[0]["distancia"]
-        distancia_max_m = linhas_ordenadas[-1]["distancia"]
-
-        # 3. Simula trajetória ponto a ponto e acumula custo
-        x_sim = self.x_ant  # no referencial do robô: (0, 0)
-        y_sim = self.y_ant
-        custo_total = 0.0
-        passos = 1
-        matriz_concluida = False
-        fora_matriz_horizontal = False
-        celulas_ocupadas = []
-
-        for _ in range(passos):
-            # Avança no referencial do robô
-            x_sim += distancia_m * np.sin(-theta_rad)
-            y_sim += distancia_m * np.cos(theta_rad)
-
-            # Verifica limites de profundidade
-            if y_sim < distancia_min_m:
-                continue  # fora da matriz
-            elif y_sim > distancia_max_m:
-                matriz_concluida = True
-                break
-
-            # Localiza a linha mais próxima da profundidade atual
-            linha_alvo = next(
-                (linha["idx"] for linha in reversed(linhas_ordenadas) if y_sim >= linha["distancia"]),
-                linhas_ordenadas[0]["idx"]
-            )
-            escala_x = linhas_info[linha_alvo]["escala_x"]
-            largura_total = escala_x * largura
-            x_centralizado = x_sim + largura_total / 2
-            coluna_alvo = int(x_centralizado / escala_x)
-
-            #if 0 <= linha_alvo < altura and 0 <= coluna_alvo < largura:
-            #    custo_total += matriz_custo[linha_alvo][coluna_alvo]["custo"]
-
-            escala_x = matriz_custo[linha_alvo][0]["escala_x"]
-            colunas_ocupadas = int(np.ceil(largura_equipamento / escala_x))
-            colunas_por_lado = colunas_ocupadas // 2
-
-            # 2. Soma os custos ao longo do caminho (de trás até a frente)
-            for i in range(altura - 1, linha_alvo - 1, -1):
-                for j in range(coluna_alvo - colunas_por_lado, coluna_alvo + colunas_por_lado + 1):
-                    if 0 <= i < altura and 0 <= j < largura:
-                        celulas_ocupadas.append((i, j))
-                        custo_total += matriz_custo[i][j]["custo"]
-                    #else:
-                    #    custo_total += 20.0
-                    #    fora_matriz_horizontal = True
-
-            mostrar_log(f"d: {self.y_ant:.2f} m, {tipo.name} {np.degrees(angulo):.2f} - Posicao ({linha_alvo}, {coluna_alvo}), x_sim: {x_sim}, y_sim: {y_sim}, Custo: {round(custo_total, 4)}, Theta: {np.degrees(theta_rad):.2f}, Passo: {round(passos * distancia_m, 2)} m") #, celulas: {celulas_ocupadas}
-
-        #if fora_matriz_horizontal and len(celulas_ocupadas) == 0:
-        #    matriz_concluida = True
-        #    custo_total = float('inf')
-
-        return custo_total, matriz_concluida
-
-
-    # MPC MIOPE
-
-    def compute(self, contexto, comando_anterior):
         try:
-            agora = time.time()
-            self.dt = 0.2 if self.ultima_atualizacao == None else agora - self.ultima_atualizacao
-            self.ultima_atualizacao = agora
-            comando = self._processar_mpc(contexto, comando_anterior)
-            mostrar_log(f"Estatisticas MPC: dt = {round(self.dt, 2)} s, freq = {round(1.0 / self.dt, 2)} Hz")
-            return comando
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao processar compute MPC: {e}")
+            peso_erro_pos = 1.2  # 0
+            peso_erro_ori = 1.0  # 1
+            peso_suavidade = 1.4 # 2
+            peso_fator_re = 0.2  # 3
+            peso_ideal = 0.4     # 4
 
-    def _processar_mpc(self, contexto, comando_anterior):
-        try:
-            if not self.pontos_info:
-                return None
-            
-            GPS = contexto.get("GPS", {})
-            pos_lat = GPS.get("Latitude", 0)
-            pos_lon = GPS.get("Longitude", 0)
-            theta = np.radians(GPS.get("AnguloCarro", 0))
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
+            CARRO = contexto.get("Carro", {})
+            status = StatusCarroMapa(CARRO.get("Status", StatusCarroMapa.Parado.value))
+            dentro = CARRO.get("DentroCorredor", False)
+            manobrando = CARRO.get("ManobrandoEntreRuas", False)
+            velocidade = CARRO.get("Velocidade", self.velocidade_min)
 
-            x, y = self.gps_handler.converter_latlon_para_xz(pos_lat, pos_lon)
+            # Penalidade proporcional à velocidade
+            penalidade_por_velocidade = (velocidade / self.velocidade_max) / 10.0
+            erro_ori_deg = np.degrees(erro_ori_rad)
+            erro_ori_clipped = max(min(erro_ori_deg, self.angulo_max_graus), -self.angulo_max_graus)
+            erro_ori_norm = abs(erro_ori_clipped / self.angulo_max_graus)
 
-            x_plot = x
-            y_plot = y
-            sim = [(x, y, theta)]
-            x_temp, y_temp, theta_temp = x, y, theta
-
-            atraso_simulado_passos = 0  # Delay estimado de resposta do robô
-
-            distancia_m = velocidade * self.dt
-
-            if distancia_m > 0:
-                pontos_horizonte = int(self.horizonte / (distancia_m))
-            else:
-                pontos_horizonte = 10
-
-            pontos_horizonte = max(10, min(pontos_horizonte, 120))  # entre 10 e 120 pontos
-
-            pontos_horizonte = self.horizonte_passos
-
-            angulo_final = None
-            tipo_final = None
-            self.x_ant = 0
-            self.y_ant = 0
-            self.dist_acumulada = 0
-
-            for i in range(pontos_horizonte):
-                primeira_execucao = angulo_final is None and tipo_final is None
-                if primeira_execucao:
-                    idx_alvo = self._corrigir_pontos_visitados(x_temp, y_temp, self.visitados_execucao)
-                else:
-                    idx_alvo = self._corrigir_pontos_visitados(x_temp, y_temp, deepcopy(self.visitados_execucao))
-                if idx_alvo >= len(self.pontos_info):
-                    angulo_final, tipo_final = comando_parado()
-                    break
-                if i < atraso_simulado_passos and comando_anterior:
-                    angulo_mpc = np.radians(comando_anterior["angulo"])
-                    tipo_mpc = TipoMovimentoDirecional(comando_anterior["tipo"])
-                else:
-                    ponto_info = self.pontos_info[idx_alvo]
-                    tipo_ponto = TipoPontoRua(ponto_info.get("tipo"))
-                    ponto_alvo = ponto_info["xy"]
-                    angulo_mpc, tipo_mpc = self._calcular_melhor_candidato(ponto_alvo, x_temp, y_temp, theta_temp, deepcopy(self.visitados_execucao), contexto, comando_anterior, primeira_execucao)
-                    if primeira_execucao:
-                        angulo_final = angulo_mpc
-                        tipo_final = tipo_mpc
-
-                omega = self.gps_handler.calcular_omega(velocidade, angulo_mpc, tipo_mpc)
-
-                # Simula a movimentação usando theta com base rotacionada (-theta + 90)
-                theta_plot = (-theta_temp) + np.radians(90)
-                x_temp += velocidade * np.cos(theta_temp) * self.dt
-                y_temp += velocidade * np.sin(theta_temp) * self.dt
-                theta_ant = theta_temp
-                theta_temp += omega * self.dt
-                delta_theta = (theta_ant - theta_temp + np.pi) % (2 * np.pi) - np.pi
-                
-                self.x_ant += distancia_m * np.sin(-delta_theta)
-                self.y_ant += distancia_m * np.cos(delta_theta)
-
-                comando_anterior["angulo"] = angulo_mpc
-                comando_anterior["tipo"] = tipo_mpc
-
-                x_plot += velocidade * np.cos(theta_plot) * self.dt
-                y_plot += velocidade * np.sin(theta_plot) * self.dt
-
-                sim.append((x_plot, y_plot, theta_plot))
-
-            # Converte os pontos simulados para lat/lon
-            simulacao_latlon = []
-            for sx, sy, theta in sim:
-                dlat = sy / self.gps_handler.raio_terra
-                dlon = sx / (self.gps_handler.raio_terra * np.cos(np.radians(self.lat0)))
-                lat = self.lat0 + np.degrees(dlat)
-                lon = self.lon0 + np.degrees(dlon)
-                simulacao_latlon.append([lat, lon, theta])
-
-            #angulo_final, tipo_final = self._calcular_melhor_candidato(sim[0][0], sim[0][1], theta, self.visitados_execucao, contexto, comando_anterior, False)
-
-            return {
-                "angulo": np.degrees(angulo_final),
-                "tipo": tipo_final,
-                "simulacao": simulacao_latlon,
-                "parada_necessaria": False
+            custo_movimento = {
+                TipoMovimentoDirecional.RodasDianteiras: 0.0,
+                TipoMovimentoDirecional.MovimentoArco: 0.0
             }
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao processar MPC: {e}")
 
-    def _calcular_melhor_candidato(self, ponto_alvo, x, y, theta, visitados, contexto, comando_anterior, primeira_execucao):
-        try:
-            orient = self.gps_handler.calcular_orientacao(ponto_alvo, (x, y))
-            delta_theta = (orient - theta + np.pi) % (2 * np.pi) - np.pi
-            erro_ori = abs(delta_theta)
-            angulo = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-
-            melhor_custo = float('inf')
-            melhor_tipo = TipoMovimentoDirecional.RodasDianteiras
-            melhor_angulo = angulo
-
-            tipos_validos, angulos_candidatos = self._gerar_angulos_candidatos(angulo, erro_ori, contexto)
-
-            velocidade = velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-            deslocamento = velocidade * self.dt
-
-            ctx_vw = contexto.get("VisualWorker", {})
-            vw_ativado = ctx_vw.get("Ativado", False)
-            vw_operante = ctx_vw.get("Operante", False)
-            com_matriz_custo = contexto.get("RegrasAtivas", {}).get("matriz_custo", False)
-            filtrar_matriz = vw_ativado and vw_operante and com_matriz_custo
-
-            if filtrar_matriz:
-                matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", [[]])
-                # 1. Analisa profundidade média por linha
-                linhas_info = analisar_linhas_por_profundidade(matriz_custo, "distancia_m", contexto.get("VisualWorker", {}).get("Camera", {}).get("FovH", 0.7))
-
-            for tipo in tipos_validos:
-                for angulo_testado in angulos_candidatos:
-                    omega = self.gps_handler.calcular_omega(velocidade, angulo_testado, tipo)
-                    theta_sim = theta + omega * self.dt
-                    delta_theta_sim = (theta - theta_sim + np.pi) % (2 * np.pi) - np.pi
-                    x_sim = x + (np.cos(theta_sim) * deslocamento)
-                    y_sim = y + (np.sin(theta_sim) * deslocamento)
-
-                    custo = 0.0
-
-                    if filtrar_matriz:
-                        custo_espaco, matriz_concluida = self._simular_trajetoria_matriz_custo(matriz_custo, linhas_info, tipo, angulo_testado, deslocamento, delta_theta_sim)
-                        custo += custo_espaco
-
-                    if custo > melhor_custo:
-                        continue
-
-                    idx_alvo_sim = self._corrigir_pontos_visitados(x_sim, y_sim, deepcopy(visitados))
-                    
-                    ponto_alvo_sim = self.pontos_info[idx_alvo_sim]["xy"]
-                    orient_sim = self.gps_handler.calcular_orientacao((x_sim, y_sim), ponto_alvo_sim)
-                    orient_sim = (orient_sim + np.pi) % (2 * np.pi)
-                    erro_pos = np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]])
-                    erro_ori_sim = self._erro_angular(orient_sim, theta_sim)
-                    pesos = self._calcular_pesos_movimento(contexto, erro_ori_sim)
-                    peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_ideal, peso_movimento = pesos[0], pesos[1], pesos[2], pesos[3], pesos[4], pesos[5]
-                    vetor_alvo = np.array(ponto_alvo_sim) - np.array([x_sim, y_sim])
-                    vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
-                    vetor_movel = np.array([np.cos(theta_sim), np.sin(theta_sim)])
-                    cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                    delta_angulo = abs(angulo_testado - np.radians(comando_anterior.get("angulo", 0)))
-                    custo_pos = erro_pos * peso_erro_pos
-                    custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
-                    custo_suavidade = ((delta_angulo / np.pi) * erro_pos) * peso_suavidade
-                    custo_tipo_movimento = peso_movimento[tipo] # * erro_pos
-                    erro_re = (1 - cos_angulo) if cos_angulo > 0 else cos_angulo
-                    custo_re = -erro_re * erro_pos * peso_fator_re
-                    erro_ideal = abs(angulo_testado - angulo)
-                    custo_ideal = peso_ideal * erro_ideal
-                    custo_mapa = (custo_pos + custo_ori + custo_suavidade + custo_tipo_movimento + custo_ideal + custo_re) / 10.0
-                    custo += custo_mapa
-                    
-                    #if primeira_execucao:
-                    #    mostrar_log(f"{tipo.name} {round(np.degrees(angulo_testado), 2)}°, d: {round(deslocamento, 2)} m - iPA: {idx_alvo_sim}, Custo: {round(custo, 4)}, Omega: {round(np.degrees(omega), 2)}°, Theta: {round(np.degrees(theta_sim), 2)}°, Ori: {round(np.degrees(orient_sim), 2)}°, E Ori: {round(np.degrees(erro_ori_sim), 3)}°, E Pos: {round(erro_pos, 3)}, F Re: {round(erro_re, 3)}, d°: {round(np.degrees(delta_angulo), 2)}, CE: {round(custo_espaco, 4)}, CP: {round(custo_pos, 4)}, CO: {round(custo_ori, 4)}, CM: {round(custo_tipo_movimento, 4)}, CS: {round(custo_suavidade, 4)}, CR: {round(custo_re, 4)}, CI: {round(custo_ideal, 4)}")
-                    if custo < melhor_custo:
-                        melhor_custo = custo
-                        melhor_tipo = tipo
-                        melhor_angulo = angulo_testado
-
-                #if primeira_execucao:
-                #    mostrar_log(f"COMANDO: {tipo.name}, Angulo ideal: {round(np.degrees(angulo), 2)}, Melhor angulo: {round(np.degrees(melhor_angulo), 2)}, Custo: {round(melhor_custo, 3)}")
-
-            mostrar_log(f"z: {self.y_ant:.3f} m, x: {self.x_ant:.3f} m, COMANDO: {melhor_tipo.name}, Angulo ideal: {round(np.degrees(angulo), 2)}, Melhor angulo: {round(np.degrees(melhor_angulo), 2)}, Custo: {round(melhor_custo, 3)}")
-            return melhor_angulo, melhor_tipo
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao calcular melhor candidato: {e}")
-    
-    
-
-    # MPC COM CANDIDATO FIXO DURANTE O HORIZONTE
-
-    def compute_fixo(self, contexto, comando_anterior):
-        try:
-            agora = time.time()
-            self.dt = 0.2 if self.ultima_atualizacao == None else agora - self.ultima_atualizacao
-            self.ultima_atualizacao = agora
-            comando = self._processar_mpc_fixo(contexto, comando_anterior)
-            mostrar_log(f"Estatisticas MPC: dt = {round(self.dt, 2)} s, freq = {round(1.0 / self.dt, 2)} Hz")
-            return comando
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao processar compute MPC: {e}")
-
-    def _processar_mpc_fixo(self, contexto, comando_anterior):
-        try:
-            if not self.pontos_info:
-                return None
-            GPS = contexto.get("GPS", {})
-            pos_lat = GPS.get("Latitude", 0)
-            pos_lon = GPS.get("Longitude", 0)
-            theta = np.radians(GPS.get("AnguloCarro", 0))
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-
-            x, y = self.gps_handler.converter_latlon_para_xz(pos_lat, pos_lon)
-
-            x_temp, y_temp, theta_temp = x, y, theta
-
-            distancia_m = velocidade * self.dt
-
-            if distancia_m > 0:
-                pontos_horizonte = int(self.horizonte / (distancia_m))
+            if manobrando or status in [ StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando ]:
+                custo_movimento[TipoMovimentoDirecional.MovimentoArco] += 0.0
+                custo_movimento[TipoMovimentoDirecional.RodasDianteiras] += 1.0
             else:
-                pontos_horizonte = 10
+                custo_movimento[TipoMovimentoDirecional.MovimentoArco] += 0.0 if erro_ori_norm >= 0.4 else 1.0
+                custo_movimento[TipoMovimentoDirecional.RodasDianteiras] += 1.2 if erro_ori_norm >= 0.4 else 0.0
 
-            pontos_horizonte = max(10, min(pontos_horizonte, 50))  # entre 10 e 50 pontos
-
-            self.horizonte_passos = pontos_horizonte
-
-            angulo_final = None
-            tipo_final = None
-            self.x_ant = 0
-            self.y_ant = 0
-            self.dist_acumulada = 0
-            idx_alvo = contexto.get("Carro", {}).get("IdxProximoPonto", 0)
-            if idx_alvo >= len(self.pontos_info):
-                angulo_final, tipo_final = comando_parado()
-            else:
-                ponto_info = self.pontos_info[idx_alvo]
-                ponto_alvo = ponto_info["xy"]
-                angulo_final, tipo_final, sim = self._calcular_melhor_candidato_horizonte_fixo(ponto_alvo, x_temp, y_temp, theta_temp, deepcopy(self.visitados_execucao), contexto, comando_anterior)
-
-            comando_anterior["angulo"] = angulo_final
-            comando_anterior["tipo"] = tipo_final
-
-            # Converte os pontos simulados para lat/lon
-            simulacao_latlon = []
-            for sx, sy in sim:
-                dlat = sy / self.gps_handler.raio_terra
-                dlon = sx / (self.gps_handler.raio_terra * np.cos(np.radians(self.lat0)))
-                lat = self.lat0 + np.degrees(dlat)
-                lon = self.lon0 + np.degrees(dlon)
-                simulacao_latlon.append([lat, lon])
-
-            return {
-                "angulo": np.degrees(angulo_final),
-                "tipo": tipo_final,
-                "simulacao": simulacao_latlon
-            }
-        
+            return (peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_ideal, custo_movimento)
         except Exception as e:
-            mostrar_log(f"❌ Erro ao processar MPC: {e}")
-
-    def _calcular_melhor_candidato_horizonte_fixo(self, ponto_alvo, x, y, theta, visitados, contexto, comando_anterior):
-        try:
-            orient = self.gps_handler.calcular_orientacao(ponto_alvo, (x, y))
-            delta_theta = (orient - theta + np.pi) % (2 * np.pi) - np.pi
-            erro_ori = abs(delta_theta)
-            angulo = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-
-            melhor_custo = float('inf')
-            melhor_tipo = TipoMovimentoDirecional.RodasDianteiras
-            melhor_angulo = angulo
-
-            tipos_validos, angulos_candidatos = self._gerar_angulos_candidatos(angulo, erro_ori, contexto)
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-            manobrando = contexto.get("Carro", {}).get("ManobrandoEntreRuas", False)
-            distancia_m = velocidade * self.dt
-            matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", [[]])
-
-            simulacoes = {}
-
-            # 1. Analisa profundidade média por linha
-            linhas_info = analisar_linhas_por_profundidade(matriz_custo, "distancia_m", contexto.get("VisualWorker", {}).get("Camera", {}).get("FovH", 0.7))
-
-            for tipo in tipos_validos:
-                for angulo_testado in angulos_candidatos:
-                    custo_total = 0.0
-                    x_sim, y_sim, theta_sim = x, y, theta
-                    x_plot, y_plot = x, y
-                    self.x_ant, self.y_ant = 0.0, 0.0
-                    chave = (tipo, angulo_testado)
-                    simulacoes[chave] = []
-                    comando_anterior_copy = deepcopy(comando_anterior)
-                    omega = self.gps_handler.calcular_omega(velocidade, angulo_testado, tipo)
-                    for _ in range(self.horizonte_passos):
-                        theta_sim += omega * self.dt
-                        x_sim += np.cos(theta_sim) * distancia_m
-                        y_sim += np.sin(theta_sim) * distancia_m
-
-
-                        theta_plot = (-theta_sim) + np.radians(90)
-                        theta_ant = theta_sim - omega * self.dt
-                        delta_theta = (theta_ant - theta_sim + np.pi) % (2 * np.pi) - np.pi
-                        x_plot += velocidade * np.cos(theta_plot) * self.dt
-                        y_plot += velocidade * np.sin(theta_plot) * self.dt
-                        simulacoes[chave].append((x_plot, y_plot))
-
-
-                        delta_theta_sim = (theta - theta_sim + np.pi) % (2 * np.pi) - np.pi
-                        custo_espaco, matriz_concluida = self._simular_trajetoria_matriz_custo(matriz_custo, linhas_info, tipo, angulo_testado, distancia_m, delta_theta_sim)
-
-                        custo_total += custo_espaco
-
-                        self.x_ant += distancia_m * np.sin(-delta_theta_sim)
-                        self.y_ant += distancia_m * np.cos(delta_theta_sim)
-
-                        # Avaliação final da posição no final da simulação
-                        idx_alvo_sim = self._corrigir_pontos_visitados(x_sim, y_sim, deepcopy(visitados))
-                        ponto_alvo_sim = self.pontos_info[idx_alvo_sim]["xy"]
-                        orient_sim = self.gps_handler.calcular_orientacao((x_sim, y_sim), ponto_alvo_sim)
-                        orient_sim = (orient_sim + np.pi) % (2 * np.pi)
-
-                        erro_pos = np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]])
-                        erro_ori_sim = self._erro_angular(orient_sim, theta_sim)
-
-                        peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_movimento, peso_ideal = self._calcular_pesos_movimento(contexto, erro_ori_sim, erro_pos)
-
-                        vetor_alvo = np.array(ponto_alvo_sim) - np.array([x_sim, y_sim])
-                        vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
-                        vetor_movel = np.array([np.cos(theta_sim), np.sin(theta_sim)])
-                        cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                        delta_angulo = abs(angulo_testado - comando_anterior_copy.get("angulo", 0))
-
-                        custo_pos = erro_pos * peso_erro_pos
-                        custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
-                        custo_suavidade = ((delta_angulo / np.pi) * erro_pos) * peso_suavidade
-                        custo_tipo_movimento = peso_movimento[tipo]
-                        erro_re = (1 - cos_angulo) if cos_angulo > 0 else cos_angulo
-                        custo_re = -erro_re * erro_pos * peso_fator_re
-                        erro_ideal = abs(angulo_testado - angulo)
-                        custo_ideal = peso_ideal * erro_ideal
-                        custo_mapa = (custo_pos + custo_ori + custo_suavidade + custo_tipo_movimento + custo_ideal + custo_re) / 10.0
-                        if manobrando:
-                            custo_mapa *= 20.0
-
-                        custo_total += custo_mapa
-
-                        comando_anterior_copy["angulo"] = angulo_testado
-                        comando_anterior_copy["tipo"] = tipo
-
-                        if matriz_concluida:
-                            break
-
-                    if custo_total < melhor_custo:
-                        melhor_custo = custo_total
-                        melhor_tipo = tipo
-                        melhor_angulo = angulo_testado
-
-            melhor_trajetoria = simulacoes.get((melhor_tipo, melhor_angulo), [])
-            mostrar_log(f"📈 HORIZONTE FIXO | COMANDO: {melhor_tipo.name}, Angulo ideal: {round(np.degrees(angulo), 2)}, Melhor angulo: {round(np.degrees(melhor_angulo), 2)}, Custo Total: {round(melhor_custo, 4)}")
-            return melhor_angulo, melhor_tipo, melhor_trajetoria
-
-        except Exception as e:
-            mostrar_log(f"❌ Erro no MPC de horizonte fixo: {e}")
-
-
-
-    # MPC AVANCADO COM PREVISAO DE HORIZONTE
-
-    def compute_horizonte(self, contexto, comando_anterior):
-        agora = time.time()
-        self.dt = 0.2 if self.ultima_atualizacao == None else agora - self.ultima_atualizacao
-        self.ultima_atualizacao = agora
-
-        GPS = contexto.get("GPS", {})
-        pos_lat = GPS.get("Latitude", 0)
-        pos_lon = GPS.get("Longitude", 0)
-        theta = np.radians(GPS.get("AnguloCarro", 0))
-        x, y = self._latlon_to_xy(pos_lat, pos_lon, self.lat0, self.lon0)
-        angulo_final, tipo_final = self._calcular_melhor_candidato_horizonte(
-            x, y, theta,
-            self.visitados_execucao.copy(),
-            contexto,
-            comando_anterior
-        )
-        comando = {
-            "angulo": np.degrees(angulo_final), 
-            "tipo": tipo_final,
-            "simulacao": []
-        }
-        return comando
-
-    def _calcular_melhor_candidato_horizonte(self, x, y, theta, visitados_sim, contexto, comando_anterior):
-        idx_alvo = self._corrigir_pontos_visitados(x, y, visitados_sim)
-        if idx_alvo >= len(self.pontos_info):
-            return comando_parado()
-
-        ponto_info = self.pontos_info[idx_alvo]
-        ponto_alvo = ponto_info["xy"]
-        orient = self.gps_handler.calcular_orientacao(ponto_alvo, (x, y))
-        delta_theta = (orient - theta + np.pi) % (2 * np.pi) - np.pi
-        erro_ori = abs(delta_theta)
-        angulo_ideal = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-
-        melhor_custo = float("inf")
-        melhor_tipo = None
-        melhor_angulo = None
-
-        tipos_validos, angulos_candidatos = self._gerar_angulos_candidatos(angulo_ideal, erro_ori, contexto)
-
-        for tipo in tipos_validos:
-            for angulo in angulos_candidatos:
-                #custo, simulacoes = self.simular_passos_recursivo(x, y, theta, self.horizonte_passos, 0, visitados_sim.copy(), contexto, 0, melhor_custo, comando_anterior, angulo_ideal, erro_ori)
-                custo = self.simular_passos(x, y, theta, self.horizonte_passos, visitados_sim.copy(), contexto, { "angulo": angulo, "tipo": tipo }, 0, melhor_custo, comando_anterior, angulo_ideal)
-                #print(f"Tipo: {tipo.name}, Angulo: {np.degrees(angulo)}, Simulacoes: {simulacoes}: Custo: {custo}")
-                #input("⏸️ Pressione Enter para continuar...")
-                if custo < melhor_custo:
-                    melhor_custo = custo
-                    melhor_tipo = tipo
-                    melhor_angulo = angulo
-
-        return melhor_angulo, melhor_tipo
-
-    def simular_passos(self, x, y, theta, passos_restantes, visitados_sim, contexto, comando, custo_acumulado, custo_melhor, comando_anterior, angulo_ideal):
-        if passos_restantes == 0:
-            return custo_acumulado
-
-        tipo = comando["tipo"]
-        angulo_testado = comando["angulo"]
-        velocidade = contexto["Carro"].get("Velocidade", 0)
-        dt = self.dt
-
-        idx_alvo = self._corrigir_pontos_visitados(x, y, visitados_sim.copy())
-        ponto_alvo = self.pontos_info[idx_alvo]["xy"]
-        orient = self.gps_handler.calcular_orientacao(ponto_alvo, (x, y))
-        delta_theta = (orient - theta + np.pi) % (2 * np.pi) - np.pi
-        angulo = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-
-        omega = self.gps_handler.calcular_omega(velocidade, angulo_testado, tipo)
-        theta_sim = theta + omega * dt
-        x_sim = x - velocidade * np.cos(theta_sim) * dt
-        y_sim = y - velocidade * np.sin(theta_sim) * dt
-        
-        idx_alvo_sim = self._corrigir_pontos_visitados(x_sim, y_sim, visitados_sim)
-        ponto_alvo_sim = self.pontos_info[idx_alvo_sim]["xy"]
-        if np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]]) < self.pontos_info[idx_alvo_sim].get("distanciaMargem", 0.7):
-            visitados_sim[idx_alvo_sim] = True
-        orient_sim = self.gps_handler.calcular_orientacao(ponto_alvo_sim, (x_sim, y_sim))
-
-        erro_pos = np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]])
-        erro_ori_sim = self._erro_angular(orient_sim, theta_sim)
-
-        vetor_alvo = np.array([x_sim, y_sim]) - np.array(ponto_alvo_sim)
-        vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
-        vetor_movel = np.array([np.cos(theta_sim), np.sin(theta_sim)])
-        cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                
-        delta_angulo = abs(angulo_testado - comando_anterior.get("angulo", 0))
-
-        peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_movimento, peso_ideal = self._calcular_pesos_movimento(contexto, erro_ori_sim, erro_pos)
-
-        custo_pos = erro_pos * peso_erro_pos
-        custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
-        custo_suavidade = ((delta_angulo / np.pi) * erro_pos) * peso_suavidade
-        erro_re = (1 - cos_angulo) if cos_angulo > 0 else cos_angulo
-        custo_re = -erro_re * erro_pos * peso_fator_re
-        if passos_restantes == self.horizonte_passos:
-            custo_tipo_movimento = peso_movimento[tipo]
-        else:
-            custo_tipo_movimento = 0
-
-        erro_ideal = abs(angulo_testado - angulo)
-        custo_ideal = peso_ideal * erro_ideal
-
-        custo = custo_pos + custo_ori + custo_suavidade + custo_tipo_movimento + custo_ideal + custo_re
-
-        custo_acumulado += custo
-
-        print(f"{passos_restantes} {tipo.name} {round(np.degrees(angulo_testado), 2)}°, iPA: {idx_alvo_sim}, Custo: {round(custo_acumulado, 4)}, Omega: {round(np.degrees(omega), 2)}°, Theta: {round(np.degrees(theta_sim), 2)}°, Ori: {round(np.degrees(orient_sim), 2)}°, E Pos: {round(erro_pos, 3)}, E Ori: {round(np.degrees(erro_ori_sim), 3)}°, F Re: {round(erro_re, 3)}, d°: {round(np.degrees(delta_angulo), 2)}, CP: {round(custo_pos, 4)}, CO: {round(custo_ori, 4)}, CM: {round(custo_tipo_movimento, 4)}, CS: {round(custo_suavidade, 4)}, CR: {round(custo_re, 4)}, CI: {round(custo_ideal, 4)}")
-
-        if custo_acumulado > custo_melhor:
-            return float('inf')
-        
-        novo_comando_anterior = {
-            "angulo": comando["angulo"],
-            "tipo": comando["tipo"]
-        }
-
-        return self.simular_passos(x_sim, y_sim, theta_sim, passos_restantes - 1, visitados_sim, contexto, comando, custo_acumulado, custo_melhor, novo_comando_anterior, angulo_ideal)
-
-    def simular_passos_recursivo(self, x, y, theta, passos_restantes, simulacoes, visitados_sim, contexto, custo_acumulado, custo_melhor, comando_anterior, angulo_ideal, erro_ori):
-        if passos_restantes == 0:
-            return custo_acumulado, simulacoes
-
-        velocidade = contexto["Carro"].get("Velocidade", 0)
-        dt = self.dt
-        melhor_custo = float("inf")
-
-        tipos_validos, angulos_candidatos = self._gerar_angulos_candidatos(angulo_ideal, erro_ori, contexto)
-
-        for tipo in tipos_validos:
-            for angulo in angulos_candidatos:
-                #print(f"Simulando {tipo.name} {np.degrees(angulo)}")
-                omega = self.gps_handler.calcular_omega(velocidade, angulo, tipo)
-                theta_temp = theta + omega * dt
-                x_temp = x + velocidade * np.cos(theta) * dt
-                y_temp = y + velocidade * np.sin(theta) * dt
-                visitados_temp = visitados_sim.copy()
-
-                idx_alvo = self._corrigir_pontos_visitados(x_temp, y_temp, visitados_temp)
-                if idx_alvo >= len(self.pontos_info):
-                    continue
-
-                ponto_alvo = self.pontos_info[idx_alvo]["xy"]
-                if np.linalg.norm([x_temp - ponto_alvo[0], y_temp - ponto_alvo[1]]) < self.pontos_info[idx_alvo].get("distanciaMargem", 0.7):
-                    visitados_temp[idx_alvo] = True
-
-                orient_sim = self.gps_handler.calcular_orientacao(ponto_alvo, (x_temp, y_temp))
-                erro_ori_sim = self._erro_angular(orient_sim, theta_temp)
-                erro_pos = np.linalg.norm([x_temp - ponto_alvo[0], y_temp - ponto_alvo[1]])
-
-                vetor_alvo = np.array([x_temp, y_temp]) - np.array(ponto_alvo)
-                vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
-                vetor_movel = np.array([np.cos(theta_temp), np.sin(theta_temp)])
-                cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                erro_re = (1 - cos_angulo) if cos_angulo > 0 else cos_angulo
-
-                delta_angulo = abs(angulo - comando_anterior.get("angulo", 0))
-                peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_movimento, peso_ideal = self._calcular_pesos_movimento(contexto, erro_ori_sim, erro_pos)
-
-                custo_pos = erro_pos * peso_erro_pos
-                custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
-                custo_suav = ((delta_angulo / np.pi) * erro_pos) * peso_suavidade
-                custo_mov = peso_movimento[tipo]
-                custo_re = -erro_re * erro_pos * peso_fator_re
-                custo_ideal = peso_ideal * abs(angulo - angulo_ideal)
-
-                custo_total = custo_acumulado + custo_pos + custo_ori + custo_suav + custo_mov + custo_re + custo_ideal
-
-                if custo_total > custo_melhor:
-                    continue
-
-                custo, simulacoes = self.simular_passos_recursivo(
-                    x_temp, y_temp, theta_temp, passos_restantes - 1, simulacoes + 1, visitados_temp, contexto,
-                    custo_total, custo_melhor, {"angulo": angulo, "tipo": tipo}, angulo_ideal, erro_ori
-                )
-
-                #print(f"{tipo.name} {round(np.degrees(angulo), 2)}°, iPA: {idx_alvo}, Custo: {round(custo_total, 4)}, Omega: {round(np.degrees(omega), 2)}°, Theta: {round(np.degrees(theta), 2)}°, Ori: {round(np.degrees(orient_sim), 2)}°, E Pos: {round(erro_pos, 3)}, E Ori: {round(np.degrees(erro_ori_sim), 3)}°, F Re: {round(erro_re, 3)}, d°: {round(np.degrees(delta_angulo), 2)}, CP: {round(custo_pos, 4)}, CO: {round(custo_ori, 4)}, CM: {round(custo_mov, 4)}, CS: {round(custo_suav, 4)}, CR: {round(custo_re, 4)}, CI: {round(custo_ideal, 4)}")
-
-                if custo < melhor_custo:
-                    melhor_custo = custo
-
-        return melhor_custo, simulacoes
-
-
-
-    # MPC COM GERACAO DE CANDIDATOS INTELIGENTE
-
-    def compute_inteligente(self, contexto, comando_anterior):
-        try:
-            agora = time.time()
-            self.dt = 0.2 if self.ultima_atualizacao == None else agora - self.ultima_atualizacao
-            self.ultima_atualizacao = agora
-            comando = self._processar_mpc_inteligente(contexto, comando_anterior)
-            mostrar_log(f"Estatisticas MPC: dt = {round(self.dt, 2)} s, freq = {round(1.0 / self.dt, 2)} Hz")
-            return comando
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao processar compute MPC: {e}")
-
-    def _processar_mpc_inteligente(self, contexto, comando_anterior):
-        try:
-            if not self.pontos_info:
-                return None
-            GPS = contexto.get("GPS", {})
-            pos_lat = GPS.get("Latitude", 0)
-            pos_lon = GPS.get("Longitude", 0)
-            theta = np.radians(GPS.get("AnguloCarro", 0))
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-
-            x, y = self._latlon_to_xy(pos_lat, pos_lon, self.lat0, self.lon0)
-
-            x_temp, y_temp, theta_temp = x, y, theta
-
-            distancia_m = velocidade * self.dt
-
-            self.horizonte = 1.14
-
-            if distancia_m > 0:
-                pontos_horizonte = min(int(self.horizonte / (distancia_m)), 50)
-            else:
-                pontos_horizonte = 1
-
-            self.horizonte_passos = pontos_horizonte
-            #self.horizonte_passos = 5
-
-            angulo_final = None
-            tipo_final = None
-            #self.x_ant = 0
-            #self.y_ant = 0
-            #self.dist_acumulada = 0
-            idx_alvo = contexto.get("Carro", {}).get("IdxProximoPonto", 0)
-            if idx_alvo >= len(self.pontos_info):
-                angulo_final, tipo_final = comando_parado()
-            else:
-                ponto_info = self.pontos_info[idx_alvo]
-                ponto_alvo = ponto_info["xy"]
-                idx_alvo_atual = self._corrigir_pontos_visitados(x, y, self.visitados_execucao, idx_alvo)
-                print(f"idx_alvo: {idx_alvo}, idx_alvo_visitados: {idx_alvo_atual}")
-                angulo_final, tipo_final, sim = self._calcular_melhor_candidato_inteligente(ponto_alvo, x_temp, y_temp, theta_temp, self.visitados_execucao, contexto, comando_anterior)
-
-            comando_anterior["angulo"] = angulo_final
-            comando_anterior["tipo"] = tipo_final
-
-            # Converte os pontos simulados para lat/lon
-            simulacao_latlon = []
-            for sx, sy in sim:
-                dlat = sy / self.gps_handler.raio_terra
-                dlon = sx / (self.gps_handler.raio_terra * np.cos(np.radians(self.lat0)))
-                lat = self.lat0 + np.degrees(dlat)
-                lon = self.lon0 + np.degrees(dlon)
-                simulacao_latlon.append([lat, lon])
-
-            return {
-                "angulo": np.degrees(angulo_final),
-                "tipo": tipo_final,
-                "simulacao": simulacao_latlon
-            }
-        
-        except Exception as e:
-            mostrar_log(f"❌ Erro ao processar MPC: {e}")
-
-    def _calcular_melhor_candidato_inteligente(self, ponto_alvo, x, y, theta, visitados, contexto, comando_anterior):
-        try:
-            orient = self._calcular_orientacao(ponto_alvo, (x, y))
-            delta_theta = (orient - theta + np.pi) % (2 * np.pi) - np.pi
-            erro_ori = abs(delta_theta)
-            angulo = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-
-            melhor_custo = float('inf')
-            melhor_tipo = TipoMovimentoDirecional.RodasDianteiras
-            melhor_angulo = angulo
-
-            tipos_validos, angulos_candidatos = self._gerar_angulos_candidatos_inteligente(angulo, erro_ori, contexto)
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
-            distancia_m = velocidade * self.dt
-            #manobrando = contexto.get("Carro", {}).get("ManobrandoEntreRuas", False)
-            #matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", [[]])
-
-            simulacoes = {}
-
-            # 1. Analisa profundidade média por linha
-            #linhas_info = analisar_linhas_por_profundidade(matriz_custo, "distancia_m", contexto.get("VisualWorker", {}).get("Camera", {}).get("FovH", 0.7))
-
-            for tipo in tipos_validos:
-                for angulo_testado in angulos_candidatos:
-                    custo_total = 0.0
-                    x_sim, y_sim, theta_sim = x, y, theta
-                    self.x_ant, self.y_ant = 0.0, 0.0
-                    chave = (tipo, angulo_testado)
-                    simulacoes[chave] = []
-                    comando_anterior_copy = deepcopy(comando_anterior)
-                    pontos_visitados = deepcopy(visitados)
-                    omega = self._calcular_omega(velocidade, angulo_testado, tipo)
-
-                    for passo in range(self.horizonte_passos):
-                        old_theta = theta_sim
-                        theta_sim += omega * self.dt
-                        x_sim += np.sin(theta_sim) * distancia_m
-                        y_sim += np.cos(theta_sim) * distancia_m
-
-                        simulacoes[chave].append((x_sim, y_sim))
-
-                        #delta_theta = (old_theta - theta_sim + np.pi) % (2 * np.pi) - np.pi
-                        #largura = contexto.get("Equipamento", {}).get("largura", 0.85)
-                        #custo_espaco, matriz_concluida = self._simular_trajetoria_matriz_custo(matriz_custo, linhas_info, tipo, angulo_testado, distancia_m, delta_theta, largura)
-                        #custo_total += custo_espaco
-                        #self.x_ant += distancia_m * np.sin(-delta_theta)
-                        #self.y_ant += distancia_m * np.cos(delta_theta)
-
-                        # Avaliação final da posição no final da simulação
-                        idx_alvo_sim = self._corrigir_pontos_visitados(x_sim, y_sim, pontos_visitados)
-                        ponto_alvo_sim = self.pontos_info[idx_alvo_sim]["xy"]
-                        orient_sim = self._calcular_orientacao((x_sim, y_sim), ponto_alvo_sim)
-                        orient_sim = (orient_sim + np.pi) % (2 * np.pi)
-
-                        erro_pos = np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]])
-                        erro_ori_sim = self._erro_angular(orient_sim, theta_sim)
-
-                        peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_movimento, peso_ideal = self._calcular_pesos_movimento(contexto, erro_ori_sim, erro_pos)
-
-                        vetor_alvo = np.array(ponto_alvo_sim) - np.array([x_sim, y_sim])
-                        vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
-                        vetor_movel = np.array([np.cos(theta_sim), np.sin(theta_sim)])
-                        cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                        delta_angulo = abs(angulo_testado - comando_anterior_copy.get("angulo", 0))
-
-                        custo_pos = erro_pos * peso_erro_pos
-                        custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
-                        custo_suavidade = ((delta_angulo / np.pi) * erro_pos) * peso_suavidade
-                        custo_tipo_movimento = peso_movimento[tipo]
-                        erro_re = (1.0 - cos_angulo) if cos_angulo > 0 else cos_angulo
-                        custo_re = -erro_re * erro_pos * peso_fator_re
-                        erro_ideal = abs(angulo_testado - angulo)
-                        custo_ideal = peso_ideal * erro_ideal
-                        custo_mapa = (custo_pos + custo_ori + custo_suavidade + custo_tipo_movimento + custo_ideal + custo_re)
-
-                        custo_total += custo_mapa
-
-                        comando_anterior_copy["angulo"] = angulo_testado
-                        comando_anterior_copy["tipo"] = tipo
-
-                        #if matriz_concluida:
-                        #    break
-
-                        #print(f"Passo: {passo}, Alvo: {idx_alvo_sim}, Distancia: {((passo + 1) * distancia_m):.2f} m, Theta: {theta_sim:.2f}°, DX: {(x - x_sim):.2f} m, DY: {(y - y_sim):.2f} m, y_sim: {y_sim:.4f}, x_sim: {x_sim:.4f}, Tipo: {tipo.name}, Angulo: {np.degrees(angulo_testado):.2f}, custo: {custo_total}")
-                        #mostrar_log(f"Omega: {np.degrees(omega):.2f}°, Ori: {np.degrees(orient_sim):.2f}°, E Ori: {np.degrees(erro_ori_sim):.3f}°, E Pos: {erro_pos:.3f}, F Re: {erro_re:.3f}, d°: {np.degrees(delta_angulo):.2f}, CM: {custo_mapa:.4f}, CP: {custo_pos:.4}, CO: {custo_ori:.4f}, CT: {custo_tipo_movimento:.4f}, CS: {custo_suavidade:.4f}, CR: {custo_re:.4f}, CI: {custo_ideal:.4f}")
-
-                    if custo_total < melhor_custo:
-                        melhor_custo = custo_total
-                        melhor_tipo = tipo
-                        melhor_angulo = angulo_testado
-
-            melhor_trajetoria = simulacoes.get((melhor_tipo, melhor_angulo), [])
-            mostrar_log(f"📈 HORIZONTE FIXO INTELIGENTE | COMANDO: {melhor_tipo.name}, Angulo ideal: {np.degrees(angulo):.2f}, Melhor angulo: {np.degrees(melhor_angulo):.2f}, Custo Total: {melhor_custo:.4f}")
-            return melhor_angulo, melhor_tipo, melhor_trajetoria
-
-        except Exception as e:
-            mostrar_log(f"❌ Erro no MPC de horizonte fixo inteligente: {e}")
-
-    def _gerar_angulos_candidatos_inteligente(self, angulo_ideal_rad, erro_ori, contexto):
-        def clamp(v, vmin, vmax):
-            return max(vmin, min(vmax, v))
-
-        CARRO = contexto.get("Carro", {})
-        status = CARRO.get("Status", 0)
-        manobrando = CARRO.get("ManobrandoEntreRuas", False)
-
-        if manobrando or status in [ StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando ]:
-            tipos_validos = [ TipoMovimentoDirecional.MovimentoArco ]
-        else:
-            tipos_validos = [ TipoMovimentoDirecional.RodasDianteiras, TipoMovimentoDirecional.MovimentoArco ]
-
-        erro_ori_deg = np.degrees(erro_ori)
-        angulo_ideal_deg = np.degrees(angulo_ideal_rad)
-
-        # Passo varia conforme erro angular
-        if erro_ori_deg > 10:
-            passo = 5.0
-            faixa = 15.0
-        elif erro_ori_deg > 5:
-            passo = 2.5
-            faixa = 7.5
-        else:
-            passo = 1.5
-            faixa = 4.5
-
-        # Geração em torno do ideal
-        candidatos_graus = list(np.arange(angulo_ideal_deg - faixa, angulo_ideal_deg + faixa + 0.1, passo))
-
-        # Clamping e eliminação de duplicatas
-        candidatos_radianos = sorted(set(
-            np.radians(clamp(g, -self.angulo_max_graus, self.angulo_max_graus))
-            for g in (candidatos_graus)
-        ))
-
-        #print(f"Ideal: {round(angulo_ideal_deg, 2)}°, Erro Ori: {round(erro_ori_deg, 2)}°, Obstáculos: {len(obstaculos)}, Candidatos: {[round(np.degrees(a), 2) for a in candidatos_radianos]}")
-        return tipos_validos, candidatos_radianos
-
-
+            mostrar_log(f"Erro ao calcular pesos de movimento: {e}")
 
     # MPC COM GERACAO DE CANDIDATOS RECEDING
 
     def compute_receding(self, contexto, comando_anterior):
         try:
-            agora = time.time()
-            self.dt = 0.25 if self.ultima_atualizacao is None else agora - self.ultima_atualizacao
-            #self.dt = max(min(self.dt, 0.4), 0.1)
-            _dt = self.dt
-            freq_tick = 1.0 / _dt
-            #self.dt = 0.5
+            now = time.perf_counter
+            agora = now()
+            dt_raw = 0.25 if self.ultima_atualizacao is None else (agora - self.ultima_atualizacao)
+            # EMA + clamp para evitar jitter exagerado (bom alvo: 0.12–0.30 s)
+            if not hasattr(self, "_dt_ema"):
+                self._dt_ema = dt_raw
+            self._dt_ema = 0.9*self._dt_ema + 0.1*dt_raw
+            self.dt = min(0.30, max(0.12, self._dt_ema))
             self.ultima_atualizacao = agora
+
             comando = self._processar_mpc_receding(contexto, comando_anterior)
-            
-            t_exec = max((time.time() - self.ultima_atualizacao), 1e-3)
-            #mostrar_log(f"🧭 Direcional MPC | Parada: {comando['parada_necessaria']} | Movimento: {TipoMovimentoDirecional(comando['tipo']).name} | Ângulo: {comando['angulo']}° | Horizonte: {len(comando['simulacao'])} | dt: {_dt:.2f} s | freq = {(freq_tick):.2f} Hz | t_exec: {t_exec:.2} s | f_exec: {(1.0 / t_exec):.2f} Hz")
+
+            t_exec = max((now() - self.ultima_atualizacao), 1e-3)
+            if True or comando['erro']:
+                mostrar_log(f"🧭 Direcional MPC | Erro: {comando['erro']} | Parada: {comando['parada_necessaria']} | Movimento: {TipoMovimentoDirecional(comando['tipo']).name} | Ângulo: {comando['angulo']}° | Horizonte: {len(comando['simulacao'])} | dt: {dt_raw:.2f} s | freq = {(1.0 / dt_raw):.2f} Hz | t_exec: {t_exec:.2} s | f_exec: {(1.0 / t_exec):.2f} Hz")
             return comando
         
         except Exception as e:
             mostrar_log(f"❌ Erro ao processar compute MPC: {e}")
 
     def _processar_mpc_receding(self, contexto, comando_anterior):
-        t_init = time.time()
+        now = time.perf_counter
+        t0 = now()
+
+        SLA_MS  = getattr(self, "_mpc_sla_ms", 200.0)    # 200 ms ⇒ 5 Hz
+        HEAD_MS = getattr(self, "_mpc_headroom_ms", 25.0)
+        deadline = t0 + (SLA_MS - HEAD_MS) / 1000.0
+
+        def ms_left():
+            return (deadline - now()) * 1000.0
+    
         try:
             if not self.pontos_info:
                 return None
@@ -1075,8 +406,6 @@ class ControladorMPC:
             pos_lon = GPS.get("Longitude", 0)
             pos_theta = GPS.get("AnguloCarro", 0)
             pos_passos_atraso = GPS.get("PassosAtraso", 0)
-            if (pos_passos_atraso > 0):
-                mostrar_log(f"[GPS] Atraso detectado: simulando {pos_passos_atraso} passos")
             
             theta = np.radians(pos_theta)
             velocidade = contexto.get("Carro", {}).get("Velocidade", 0)
@@ -1100,7 +429,6 @@ class ControladorMPC:
                 self.passos_horizonte_local = 0
                 self.qtd_comandos_sucessivos = 0
 
-            t_0 = time.time()
             passos_atraso = contexto.get("Carro", {}).get("PassosAtraso", 1)
             angulo_anterior = np.radians(comando_anterior.get("angulo", 0))
             tipo_anterior = TipoMovimentoDirecional(comando_anterior.get("tipo", TipoMovimentoDirecional.RodasDianteiras.value))
@@ -1108,18 +436,19 @@ class ControladorMPC:
             max_simulacoes = min(passos_atraso + pos_passos_atraso, 5)
             if max_simulacoes >= 5:
                 mostrar_log(f"[GPS] Grande atraso detectado: parando por fallback")
-                return self._comando_fallback_hot_stop(comando_anterior, True)
+                return self._comando_fallback_hot_stop(comando_anterior)
+            
+            if (pos_passos_atraso > 0):
+                mostrar_log(f"[GPS] Atraso detectado: simulando {pos_passos_atraso} passos")
 
             for passo in range(max_simulacoes):
-                if self._verifica_tempo_maximo_execucao(t_init, f"previsao_futura passo {passo}"):
+                if ms_left() <= 0:
                     return self._comando_fallback_hot_stop(comando_anterior)
-                _, posicao_futura, _ = self._simular_passo(x, y, theta, tipo_anterior, angulo_anterior, {}, { "tipo": tipo_anterior, "angulo": angulo_anterior }, contexto, deepcopy(self.visitados_execucao))
+                _, posicao_futura, _ = self._simular_passo(x, y, theta, tipo_anterior, angulo_anterior, {}, { "tipo": tipo_anterior, "angulo": angulo_anterior }, contexto, self.visitados_execucao.copy())
                 x, y, theta = posicao_futura[-1]
-            t_1 = time.time()
 
             simulacao_latlon = []
             idx_alvo_correcao = self._corrigir_pontos_visitados(x, y, self.visitados_execucao, idx_alvo_real) # marca pontos antigos como visitados
-            t_2 = time.time()
 
             #mostrar_log(f"previsao_futura em {(t_1 - t_0):.4f}s, correcao_pontos_visitados em {(t_2 - t_1):.4f}")
 
@@ -1132,86 +461,126 @@ class ControladorMPC:
                     "custo": 0.0,
                     "comandos": [(tipo_anterior, angulo_anterior)],
                     "trajetoria": [],
-                    "visitados": deepcopy(self.visitados_execucao),
+                    "visitados": self.visitados_execucao.copy(),
                     "inicial": True
                 }]
 
-                t0 = time.time()
+                # quantas expansões cabem no tempo restante deste ciclo
+                est = max(1.0, getattr(self, "_t_est_passo_ms", 6.0))
+                exp_budget = max(4, int(ms_left() // est))
+
                 for passo in range(self.qtd_comandos_sucessivos):
-                    #mostrar_log(f"simulacao_horizonte passo {passo}")
-                    if self._verifica_tempo_maximo_execucao(t_init, f"simulacao_horizonte passo {passo}"):
-                        return self._comando_fallback_hot_stop(comando_anterior)
-                    t1 = time.time()
+                    if ms_left() <= 0:
+                        break
                     novos_candidatos = []
                     for candidato in candidatos_ativos:
-                        t1_0 = time.time()
+                        # encerra passo se orçamento acabou
+                        if exp_budget <= 0 or ms_left() <= 0:
+                            break
                         x_atual, y_atual, theta_atual = candidato["x"], candidato["y"], candidato["theta"]
-                        visitados = deepcopy(candidato["visitados"])
+                        visitados = candidato["visitados"].copy()
                         cmd_anterior = {
                             "tipo": candidato["comandos"][-1][0],
                             "angulo": candidato["comandos"][-1][1],
                         }
                         idx_alvo = self._corrigir_pontos_visitados(x_atual, y_atual, visitados)
                         ponto_alvo = self.pontos_info[idx_alvo]["xy"]
-                        t2 = time.time()
-                        tipos, angs, custos_candidatos = self._gerar_angulos_candidatos_receding(ponto_alvo, (x_atual, y_atual, theta_atual), (x, y, theta), contexto)
-                        t3 = time.time()
+                        tipos, angs, custos_candidatos = self._gerar_angulos_candidatos_receding(ponto_alvo, (x_atual, y_atual, theta_atual), (x, y, theta), contexto, deadline, 15.0)
+                        if ms_left() <= 0 or exp_budget <= 0:
+                            break
 
-                        #mostrar_log(f"corrigir_pontos_visitados em {(t2 - t1_0):.4f}s, gerar_angulos_candidatos em {(t3 - t2):.4f}s")
+                        # ---------- INÍCIO BLOCO SEM THREADS ----------
+                        ang_array = np.asarray(angs, dtype=np.float32)
+                        graus_r = np.round(np.degrees(ang_array), 2)
 
-                        def simular_e_gerar(candidato, tipo, angulo):
-                            if self._verifica_tempo_maximo_execucao(t_init, f"simular_e_gerar, tipo {tipo.name}, angulo {np.degrees(angulo):.2f}"):
-                                return self._comando_fallback_hot_stop(comando_anterior)
+                        pairs_tipo, pairs_ang, pairs_heur = [], [], []
+
+                        for tipo in tipos:
+                            tv = tipo.value
+                            # heurísticos para TODOS os ângulos deste tipo
+                            h_tmp = np.array(
+                                [float(custos_candidatos.get((tv, float(g)), 0.0)) for g in graus_r],
+                                dtype=np.float32
+                            )
+                            pairs_tipo.append([tipo] * ang_array.size)
+                            pairs_ang.append(ang_array)           # sem copy
+                            pairs_heur.append(h_tmp)
+
+                        if not pairs_tipo:
+                            continue
+
+                        from itertools import chain
+                        tipos_flat = list(chain.from_iterable(pairs_tipo))
+                        angs_flat  = np.concatenate(pairs_ang, axis=0)
+                        heur_flat  = np.concatenate(pairs_heur, axis=0)
+
+                        if heur_flat.size == 0:
+                            continue
+
+                        ord_idx = np.argsort(heur_flat)
+
+                        tempo_util_ms = max(0.0, ms_left() - 10.0)
+                        est_local = max(1.0, min(getattr(self, "_t_est_passo_ms", 6.0), 20.0))
+                        Kmax = 12
+                        Kdyn = int(min(Kmax, max(3, tempo_util_ms // est_local)))
+                        Kdyn = min(Kdyn, ord_idx.size)
+
+                        melhor_custo_local = float("inf")
+
+                        for k in range(Kdyn):
+                            if ms_left() <= 0 or exp_budget <= 0:
+                                break
+
+                            i = int(ord_idx[k])
+                            tipo_k = tipos_flat[i]
+                            ang_k  = float(angs_flat[i])
+                            heur_k = float(heur_flat[i])
+
+                            if (candidato["custo"] + heur_k) > (melhor_custo_local * 1.20) and ms_left() < 2*est_local:
+                                continue
+
+                            t_c_ini = now()
                             try:
-                                custo, sim, valido = self._simular_passo(x_atual, y_atual, theta_atual, tipo, angulo, custos_candidatos, cmd_anterior, contexto, deepcopy(visitados))
-                                #print(f"{tipo.name} | angulo: {np.degrees(angulo):.2f} : Custo: {custo}")
-                                x_f, y_f, theta_f = sim[-1]
+                                custo, sim, valido = self._simular_passo(
+                                    x_atual, y_atual, theta_atual,
+                                    tipo_k, ang_k,
+                                    custos_candidatos,
+                                    cmd_anterior, contexto,
+                                    visitados  # <- sem copy aqui
+                                )
                                 if valido:
-                                    return {
+                                    x_f, y_f, theta_f = sim[-1]
+                                    novo = {
                                         "x": x_f, "y": y_f, "theta": theta_f,
-                                        "custo": candidato["custo"] + custo,
-                                        "comandos": candidato["comandos"] + [(tipo, angulo)],
+                                        "custo": candidato["custo"] + float(custo),
+                                        "comandos": candidato["comandos"] + [(tipo_k, ang_k)],
                                         "trajetoria": candidato["trajetoria"] + sim,
-                                        "visitados": deepcopy(visitados),
+                                        "visitados": visitados.copy(),  # copy apenas ao guardar
                                         "inicial": False
                                     }
-                                else:
-                                    return None
+                                    novos_candidatos.append(novo)
+                                    if novo["custo"] < melhor_custo_local:
+                                        melhor_custo_local = novo["custo"]
                             except Exception as e:
-                                mostrar_log(f"Erro ao simular e gerar passo para {tipo.name} | {np.degrees(angulo):.2f}: {e}")
-                                return None
+                                mostrar_log(f"Erro ao simular passo (sem threads) {tipo_k.name} | {np.degrees(ang_k):.2f}: {e}")
+                            finally:
+                                dt_ms = (now() - t_c_ini) * 1000.0
+                                alpha = getattr(self, "_ema_alpha", 0.2)
+                                self._t_est_passo_ms = (1.0 - alpha)*getattr(self, "_t_est_passo_ms", 6.0) + alpha*dt_ms
+                                exp_budget -= 1
+                        # ---------- FIM BLOCO SEM THREADS ----------
 
-                        futures = [self.executor.submit(simular_e_gerar, candidato, tipo, angulo) for tipo in tipos for angulo in angs]
-                        try:
-                            for fut in as_completed(futures, timeout=0.3):
-                                try:
-                                    novo = fut.result(timeout=0.05)
-                                    if novo:
-                                        novos_candidatos.append(novo)
-                                except Exception as e:
-                                    mostrar_log(f"⚠️ Timeout ou erro em future: {e}")
-                        except Exception as e:
-                            mostrar_log(f"Uma tarefa estourou timeout durante o processo de simular passo no horizonte: {e}")
+                        if exp_budget <= 0:
+                            pass  # apenas segue para seleção dos melhores
+
+                    if exp_budget <= 0 or ms_left() <= 0:
+                        break  # sai do laço de passos
 
-                        t4 = time.time()
                     # Filtro opcional: limitar para os N melhores candidatos
-                    t5 = time.time()
-                    candidatos_ativos = self._selecionar_melhores(novos_candidatos, N=5)
-                    t6 = time.time()
-                    candidatos_ativos = self._filtrar_por_margem_angular(candidatos_ativos, margem_graus=2.0)
-                    t7 = time.time()
-                    #mostrar_log(f"loop_candidatos em {(t5 - t1):.4f}s, melhores em {(t6 - t5):.4f}s, margem em {(t7 - t6):.4f}s")
-                    #print("Melhores candidatos filtrados:")
-                    #for i, c in enumerate(candidatos_ativos):
-                    #    comando = c["comandos"][-1] if c["comandos"] else ("-", 0)
-                    #    tipo = comando[0]
-                    #    angulo = np.degrees(comando[1])
-                    #    print(f"{i+1}. Tipo: {tipo.name if hasattr(tipo, 'name') else tipo}, Angulo: {round(angulo, 2)}, Custo: {round(c['custo'], 4)}")
-                    #t5 = time.time()
-                    #print(f"Passo {passo} simulado em {t5 - t1} segundos")
-                
-                t100 = time.time()
-                #print(f"Simulacao completa: {t100 - t0} segundos")
+                    candidatos_ativos = self._filtrar_por_margem_angular(novos_candidatos, margem_graus=2.0)
+                    N_top = 5 if exp_budget > 6 else 3
+                    candidatos_ativos = self._selecionar_melhores(candidatos_ativos, N=N_top)
+
                 candidatos_validos = [c for c in candidatos_ativos if not c.get("inicial", False)]
                 if len(candidatos_validos) > 0:
                     try:
@@ -1221,17 +590,16 @@ class ControladorMPC:
                         melhor = None
                 else:
                     melhor = None
-                #comandos_formatados = [(tipo.name, round(float(np.degrees(angulo)), 2)) for tipo, angulo in melhor["comandos"]]
-                #print(comandos_formatados)
-                #trajetoria_foramtada = [(round(float(_x), 2), round(float(_y), 2), round(float(np.degrees(_theta)), 2)) for _x, _y, _theta in melhor["trajetoria"]]
-                #print(trajetoria_foramtada)
-                #self.visualizador.atualizar(melhor["trajetoria"])
                 parada_necessaria = melhor is None
                 if not parada_necessaria:
-                    idx_cmd = 0 if len(melhor["comandos"]) == 1 else 1
-                    tipo_final, angulo_final = melhor["comandos"][idx_cmd]
-                    simulacao_latlon = list(self.gps_handler.converter_trajetoria_para_latlon(melhor["trajetoria"]) or [])
-                    simulacao_latlon.insert(0, (pos_lat, pos_lon, pos_theta))
+                    _comandos = melhor.get("comandos", [])
+                    if len(_comandos) == 0:
+                        pass
+                    else:
+                        idx_cmd = 0 if len(_comandos) == 1 else 1
+                        tipo_final, angulo_final = _comandos[idx_cmd]
+                        simulacao_latlon = list(self.gps_handler.converter_trajetoria_para_latlon(melhor["trajetoria"]) or [])
+                        simulacao_latlon.insert(0, (pos_lat, pos_lon, pos_theta))
                 else:
                     angulo_final = 0
                     tipo_final = TipoMovimentoDirecional.RodasDianteiras
@@ -1241,8 +609,37 @@ class ControladorMPC:
                 "angulo": float(round(np.degrees(angulo_final), 2)),
                 "tipo": tipo_final.value,
                 "simulacao": simulacao_latlon,
-                "parada_necessaria": parada_necessaria
+                "parada_necessaria": parada_necessaria,
+                "erro": False
             }
+
+
+            self._tick_id = getattr(self, "_tick_id", 0) + 1
+            if (getattr(self, "debug_cost_vis", False)
+                and contexto.get("RegrasAtivas", {}).get("matriz_custo", False)
+                and (self._tick_id % 10 == 0)):
+                dados_matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", {})
+                # reconstroi set de candidatos só pra plot, com orçamento curtinho
+                deadline_dbg = time.perf_counter() + 0.03  # ~30 ms pra não pesar
+                tipos_dbg, angs_dbg, _ = self._gerar_angulos_candidatos_receding(
+                    ponto_alvo=self.pontos_info[idx_alvo_correcao]["xy"],
+                    ponto_atual=(x, y, theta),
+                    ponto_ref=(x, y, theta),
+                    contexto=contexto,
+                    deadline=deadline_dbg,
+                    margem_ms=8.0,
+                )
+                angs_dbg_deg = [round(float(np.degrees(a)), 2) for a in angs_dbg]
+                self.debug_plot_matriz_custo(
+                    dados_matriz_custo,
+                    tipos_dbg,
+                    angs_dbg_deg,
+                    max_linhas=3,
+                    titulo=f"debug_cost_{int(self._tick_id):06d}.png",
+                    highlight=(tipo_final.value, round(float(np.degrees(angulo_final)), 2)),
+                )
+
+
             return _cmd
         
         except Exception as e:
@@ -1255,309 +652,762 @@ class ControladorMPC:
         hot_stop = delta > limite
         if hot_stop:
             mostrar_log(f"🚨 Tempo maximo de execucao excedido: {delta}/{limite}, processo: {processo}")
-            self.executor.shutdown(wait=False)
-            self.executor = ThreadPoolExecutor(max_workers=20)
         return hot_stop
 
-    def _comando_fallback_hot_stop(self, comando_anterior, parada_necessaria: bool = False):
+    def _comando_fallback_hot_stop(self, comando_anterior):
         return {
             "angulo": comando_anterior.get("angulo", 0),
             "tipo": comando_anterior.get("tipo", TipoMovimentoDirecional.RodasDianteiras.value),
             "simulacao": [],
-            "parada_necessaria": parada_necessaria
+            "parada_necessaria": True,
+            "erro": True
         }
 
-    def _gerar_angulos_candidatos_receding(self, ponto_alvo, ponto_atual, ponto_ref, contexto):
+    def _gerar_angulos_candidatos_receding(self, ponto_alvo, ponto_atual, ponto_ref, contexto, deadline, margem_ms):
+        """
+        Gera candidatos de (tipo, ângulo) respeitando o deadline do ciclo:
+        1) Ângulo ideal
+        2) Geração bruta (sem matriz: em torno do ideal; com matriz: ideal + sweep ralo)
+        3) Quick gate por LUT (descarta ângulos inviáveis, por tipo)
+        4) Ranking rápido (custo prefixado + distância ao ideal) e corte por orçamento
+        5) Filtro pesado/ranqueamento com matriz (usa 'permitidos' p/ pular combos barrados)
+        6) Uniformização em torno do ideal
+        Retorna: (tipos_final, angulos_final(rad), custos_candidatos{(tipo.value,graus_2c)->custo})
+        """
         try:
+            now = time.perf_counter
+
+            # 1) Ângulo ideal (wrap [-pi,pi] e clamp ao máx)
             orient = self.gps_handler.calcular_orientacao(ponto_alvo, ponto_atual)
             delta_theta = (orient - ponto_atual[2] + np.pi) % (2 * np.pi) - np.pi
-            angulo_ideal_rad = np.clip(delta_theta, -np.radians(self.angulo_max_graus), np.radians(self.angulo_max_graus))
-            angulo_ideal_deg = np.degrees(angulo_ideal_rad)
+            ang_max_rad = np.radians(self.angulo_max_graus)
+            angulo_ideal_rad = float(np.clip(delta_theta, -ang_max_rad, ang_max_rad))
+            angulo_ideal_deg = float(np.degrees(angulo_ideal_rad))
 
+            # 2) Tipos válidos conforme contexto
             CARRO = contexto.get("Carro", {})
             status = CARRO.get("Status", 0)
             manobrando = CARRO.get("ManobrandoEntreRuas", False)
-
-            if manobrando or status in [ StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando ]:
-                tipos_validos = [ TipoMovimentoDirecional.MovimentoArco ]
-            elif np.degrees(delta_theta) > 10.0:
-                tipos_validos = [ TipoMovimentoDirecional.RodasDianteiras, TipoMovimentoDirecional.MovimentoArco ]
+            if manobrando or status in [StatusCarroMapa.EntrandoRua, StatusCarroMapa.SaindoRua, StatusCarroMapa.Manobrando]:
+                tipos_validos = [TipoMovimentoDirecional.MovimentoArco]
+            elif abs(np.degrees(delta_theta)) > 10.0:
+                tipos_validos = [TipoMovimentoDirecional.RodasDianteiras, TipoMovimentoDirecional.MovimentoArco]
             else:
-                tipos_validos = [ TipoMovimentoDirecional.RodasDianteiras ]
+                tipos_validos = [TipoMovimentoDirecional.RodasDianteiras]
 
-            ctx_vw = contexto.get("VisualWorker", {})
-            vw_ativado = ctx_vw.get("Ativado", False)
-            vw_operante = ctx_vw.get("Operante", False)
-            com_matriz_custo = contexto.get("RegrasAtivas", {}).get("matriz_custo", False)
-            filtrar_matriz = vw_ativado and vw_operante and com_matriz_custo
+            # 3) Flags de matriz
+            dados_matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", {})
+            matriz_custo_valida = bool(dados_matriz_custo.get("Valida", False))
+            regra_matriz_custo = bool(contexto.get("RegrasAtivas", {}).get("matriz_custo", False))
+            filtrar_matriz = matriz_custo_valida and regra_matriz_custo
 
+            # 4) Geração bruta (graus)
             angulos_raw = self._gerar_candidatos_brutos(angulo_ideal_deg, filtrar_matriz)
-            #print(f"Angulos Brutos: {[round(float(a), 2) for a in angulos_raw]}")
-            tipos_final, angulos_final, custos_candidatos = self._filtrar_candidatos_validos_por_matriz(filtrar_matriz, tipos_validos, angulos_raw, angulo_ideal_rad, ponto_atual, ponto_ref, contexto)
-            #print(f"Angulos Filtrados Matriz: {[round(float(np.degrees(a)), 2) for a in angulos_final]}")
-            angulos_final = self._uniformizar_angulos_candidatos(angulos_final, angulo_ideal_rad, 10)
-            #print(f"Angulos Uniformizados: {[round(float(np.degrees(a)), 2) for a in angulos_final]}")
+            if not angulos_raw:
+                # fallback mínimo em torno do ideal
+                base = [angulo_ideal_deg + d for d in (-2.0, -1.0, 0.0, 1.0, 2.0)]
+                angulos_raw = [float(np.clip(round(b, 2), -self.angulo_max_graus, self.angulo_max_graus)) for b in base]
+
+            # ---------- QUICK GATE por LUT (por tipo) ----------
+            permitidos = None  # set de (tipo.value, grau_2c)
+            if filtrar_matriz and (deadline is None or (deadline - now()) * 1000.0 > margem_ms):
+                largura = float(contexto.get("Equipamento", {}).get("largura", 0.85))
+                L = float(contexto.get("Equipamento", {}).get("entre_eixos", 0.94))
+                self._ensure_luts(dados_matriz_custo, largura, L)
+
+                ang_min = -float(self.angulo_max_graus)
+                ang_max = +float(self.angulo_max_graus)
+                passo   = 1.0
+
+                for t in tipos_validos:
+                    if t == TipoMovimentoDirecional.MovimentoArco:
+                        self._ensure_lut_geom_por_tipo(
+                            dados_matriz_custo, largura, t, L=L,
+                            ang_min=ang_min, ang_max=ang_max, passo=passo,
+                            k_r=0.5, crab_in_phase=False
+                        )
+                    else:
+                        self._ensure_lut_geom_por_tipo(
+                            dados_matriz_custo, largura, t, L=L,
+                            ang_min=ang_min, ang_max=ang_max, passo=passo
+                        )
+
+                # Aplica gate por tipo e une permissões (qualquer tipo permitir já vale)
+                ang_all = list(angulos_raw)  # snapshot antes do filtro
+                masks = []
+                for t in tipos_validos:
+                    mask_t, _ = self._quick_gate_by_lut_tipo(angulos_raw, dados_matriz_custo, t, max_linhas=3)
+                    masks.append(mask_t)
+                if masks:
+                    mask_any = np.logical_or.reduce(masks)
+                    if mask_any.any():
+                        angulos_raw = [g for g, ok in zip(ang_all, mask_any) if ok]
+                    else:
+                        # tudo barrado → fallback mínimo ao redor do ideal
+                        ai = angulo_ideal_deg; passo_fb = 1.0
+                        angulos_raw = [float(np.clip(round(ai + d, 2), -self.angulo_max_graus, self.angulo_max_graus))
+                                    for d in (-2*passo_fb, -passo_fb, 0.0, passo_fb, 2*passo_fb)]
+
+                    # constrói set de (tipo,angulo) permitidos (apenas onde mask_any é True)
+                    allowed_angles = {float(round(g, 2)) for g, ok in zip(ang_all, mask_any) if ok}
+                    permitidos = set()
+                    for t, mask_t in zip(tipos_validos, masks):
+                        for g, ok in zip(ang_all, mask_t):
+                            g2 = float(round(g, 2))
+                            if ok and g2 in allowed_angles:
+                                permitidos.add((t.value, g2))
+
+            # ---------- RANKING RÁPIDO (barato) & LIMITE POR ORÇAMENTO ----------
+            # Score(grau) = λ * |g - ideal| + (1-λ) * custo_quick (média nas últimas linhas, melhor tipo)
+            if filtrar_matriz and angulos_raw:
+                try:
+                    C = np.asarray(dados_matriz_custo.get("Custo"), dtype=np.float32)
+                    if C is not None and C.size > 0:
+                        pref_cost = np.cumsum(C, axis=1)
+                        H, W = C.shape
+                        rows = np.arange(max(0, H - 3), H, dtype=int)  # últimas 3 linhas
+
+                        # calcula custo_quick por ângulo (melhor tipo)
+                        scores = []
+                        for g in angulos_raw:
+                            best_cost = float("inf")
+                            for t in tipos_validos:
+                                lut = getattr(self, "_lut_tipo", {}).get(t.value)
+                                if not lut:
+                                    continue
+                                k = int(self._map_ang_to_idx_fast([g], lut)[0])
+                                j0 = lut["j0"][rows, k]
+                                j1 = lut["j1"][rows, k]
+                                j0m1 = np.clip(j0 - 1, -1, W - 1)
+                                left = np.where(j0m1 >= 0, pref_cost[rows, j0m1], 0.0)
+                                right = pref_cost[rows, j1]
+                                seg_sum = (right - left).astype(np.float32)
+                                length = (j1 - j0 + 1).astype(np.float32)
+                                avg = float(np.mean(seg_sum / np.maximum(length, 1.0)))
+                                if avg < best_cost:
+                                    best_cost = avg
+                            dist_term = abs(g - angulo_ideal_deg)
+                            lamb = 0.6
+                            score = lamb * dist_term + (1.0 - lamb) * best_cost
+                            scores.append(score)
+
+                        # orçamento: quantos ângulos mandar pro filtro pesado
+                        tempo_util_ms = 0.0 if deadline is None else max(0.0, (deadline - now()) * 1000.0 - margem_ms)
+                        est = float(getattr(self, "_t_est_cand_ms", 6.0))  # ms/avaliação (ajuste se quiser)
+                        M = int(max(6, min(len(angulos_raw), tempo_util_ms // max(1.0, est))))
+
+                        if M < len(angulos_raw):
+                            ord_idx = np.argsort(np.asarray(scores))
+                            keep_idx = set(int(i) for i in ord_idx[:M])
+                            ang_keep = [angulos_raw[i] for i in range(len(angulos_raw)) if i in keep_idx]
+                            angulos_raw = ang_keep
+
+                            # restringe 'permitidos' aos ângulos mantidos
+                            if permitidos is not None:
+                                kept = {float(round(a, 2)) for a in angulos_raw}
+                                permitidos = {(tv, g) for (tv, g) in permitidos if g in kept}
+                except Exception as e:
+                    # ranking rápido é best-effort; se der algo, segue com angulos_raw como está
+                    mostrar_log(f"⚠️ Ranking rápido falhou: {e}")
+
+            # 5) Filtro pesado / matriz (usa 'permitidos' para pular combos bloqueados)
+            tipos_final, angulos_final, custos_candidatos = self._filtrar_candidatos_validos_por_matriz(
+                filtrar_matriz=filtrar_matriz,
+                tipos=tipos_validos,
+                angulos_deg=angulos_raw,          # graus
+                angulo_ideal_rad=angulo_ideal_rad,
+                p_atual=ponto_atual,
+                p_ref=ponto_ref,
+                contexto=contexto,
+                deadline=deadline,
+                margem_ms=float(margem_ms),
+                permitidos=permitidos,            # <- NOVO (pode ser None)
+            )
+
+            # 6) Uniformização ao redor do ideal (rad) com N limite
+            angulos_final = self._uniformizar_angulos_candidatos(angulos_final, angulo_ideal_rad, N=10)
 
             return tipos_final, angulos_final, custos_candidatos
+
         except Exception as e:
             mostrar_log(f"❌ Erro ao gerar candidatos receding com contexto visual: {e}")
-            return [], []
+            return [], [], {}
 
     def _gerar_candidatos_brutos(self, angulo_ideal_deg, filtrar_matriz_custo):
         try:
-            # 1. Geração em torno do ideal
-            if abs(angulo_ideal_deg) > 10:
-                passo_ideal = 5.0
-                passo_total = 3.0
+            amax = float(self.angulo_max_graus)
+            ai   = float(angulo_ideal_deg)
+            abs_ai = abs(ai)
+
+            # 1) Escolha de passos (mais denso perto do ideal quando |erro| é grande)
+            if abs_ai > 10.0:
+                passo_ideal = 2.5    # denso ao redor do ideal
                 faixa_ideal = 15.0
-            elif abs(angulo_ideal_deg) > 5:
-                passo_ideal = 2.5
-                passo_total = 7.5
-                faixa_ideal = 7.5
+                passo_sweep = 7.5    # sweep mais ralo
+            elif abs_ai > 5.0:
+                passo_ideal = 2.0
+                faixa_ideal = 10.0
+                passo_sweep = 10.0
             else:
                 passo_ideal = 1.5
-                passo_total = 10.0
                 faixa_ideal = 4.5
+                passo_sweep = 10.0
 
-            angulos_ideais = list(np.arange(angulo_ideal_deg - faixa_ideal,
-                                            angulo_ideal_deg + faixa_ideal + 0.1,
-                                            passo_ideal))
-            angulos_ideais = sorted(set(np.clip(angulos_ideais, -self.angulo_max_graus, self.angulo_max_graus)))
+            # helper sem "0.1" mágico
+            def seq(center, halfwidth, step, amax):
+                start = max(-amax, center - halfwidth)
+                stop  = min( amax, center + halfwidth)
+                span  = stop - start
+                n = int(np.floor(span / step + 1e-9)) + 1
+                return start + step * np.arange(n, dtype=np.float32)
 
-            #print(f"Angulos ideais: {[f'{float(a):.2f}' for a in angulos_ideais]}")
+            # 2) bloco ideal em torno do ângulo alvo
+            ideais = seq(ai, faixa_ideal, passo_ideal, amax)
 
-            # 2. Se for com matriz de custo, gera todo o sweep
+            # 3) sweep opcional para cobrir o range todo (quando usar matriz de custo)
             if filtrar_matriz_custo:
-                sweep = list(np.arange(-self.angulo_max_graus, self.angulo_max_graus + 0.1, passo_total))
-
-                # 3. Remove ângulos muito próximos dos ideais
-                def esta_proximo(g, lista, margem=1.5):
-                    return any(abs(g - i) < margem for i in lista)
-
-                sweep_filtrado = [g for g in sweep if not esta_proximo(g, angulos_ideais)]
-                #print(f"Sweep filtrado: {[f'{float(a):.2f}' for a in sweep_filtrado]}")
-                candidatos_graus = sorted(set(angulos_ideais + sweep_filtrado))
-                #print(f"Candidatos graus: {[f'{float(a):.2f}' for a in candidatos_graus]}")
+                sweep = seq(0.0, amax, passo_sweep, amax)  # varre [-amax, +amax]
+                # remove ângulos do sweep muito próximos dos ideais (margem 1.5°)
+                dif = np.abs(sweep[:, None] - ideais[None, :])
+                keep = (dif >= 1.5 - 1e-9).all(axis=1)
+                candidatos = np.concatenate([ideais, sweep[keep]])
             else:
-                candidatos_graus = angulos_ideais
+                candidatos = ideais
 
-            return candidatos_graus
+            # 4) normaliza/ordena com tolerância (2 casas) p/ casar com o resto do pipeline
+            candidatos = np.unique(np.round(candidatos, 2))
+            return candidatos.tolist()
 
         except Exception as e:
             mostrar_log(f"❌ Erro ao gerar candidatos brutos: {e}")
             return []
 
-    def _filtrar_candidatos_validos_por_matriz(self, filtrar_matriz, tipos, angulos_deg, angulo_ideal_rad, p_atual, p_ref, contexto):
-        pesos = self._calcular_pesos_movimento(contexto, abs(angulo_ideal_rad))
-        peso_angulo_ideal = pesos[4]
+    def _filtrar_candidatos_validos_por_matriz(
+        self,
+        filtrar_matriz,
+        tipos,
+        angulos_deg,
+        angulo_ideal_rad,
+        p_atual,
+        p_ref,
+        contexto,
+        deadline: float | None = None,
+        margem_ms: float = 20.0,
+        permitidos=None,
+    ):
+        """
+        Seleciona e avalia candidatos (tipo, ângulo) contra a matriz de custo.
+        - Respeita deadline (perf_counter absoluto) e headroom (margem_ms).
+        - Se 'permitidos' vier (set de (tipo.value, grau_2c)), ignora combos fora dele.
+        - Orçamento dinâmico por tipo com base em EMA do tempo por candidato.
+        - Degrada distância simulada quando o tempo aperta.
+        Retorna: (tipos_validos, angulos_validos(rad), custos_candidatos{(tipo.value,grau_2c)->custo_total})
+        """
+        now = time.perf_counter
+        def ms_left():
+            if deadline is None:
+                return 1e9
+            return (deadline - now()) * 1000.0
+
+        # estimadores adaptativos
+        if not hasattr(self, "_t_est_candidato_ms"):
+            self._t_est_candidato_ms = 4.0  # ms por candidato (chute inicial)
+        if not hasattr(self, "_ema_alpha"):
+            self._ema_alpha = 0.2
+
         try:
-            angulos_rad = [np.radians(g) for g in angulos_deg]
-            matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", [[]])
-            
-            if not filtrar_matriz or len(matriz_custo[0]) == 0:
+            # ---- pesos heurísticos (erro ao ideal)
+            peso_angulo_ideal = float(self._calcular_pesos_movimento(contexto, abs(angulo_ideal_rad))[4])
+
+            # ---- dados da matriz / ensure LUTs (idempotente)
+            dados_matriz_custo = contexto.get("VisualWorker", {}).get("MatrizCusto", {})
+            custo_grid = dados_matriz_custo.get("Custo", None) if isinstance(dados_matriz_custo, dict) else dados_matriz_custo
+
+            # conversões básicas
+            angulos_deg = [float(a) for a in angulos_deg]
+            angulos_rad = np.deg2rad(np.asarray(angulos_deg, dtype=np.float32))
+            graus_2c    = np.round(np.asarray(angulos_deg, dtype=np.float32), 2)
+
+            # fallback sem matriz ou sem tempo
+            if (not filtrar_matriz) or (custo_grid is None) or (np.size(custo_grid) == 0) or (ms_left() <= 0.0):
+                custos_candidatos = {}
+                erro = np.abs(angulos_rad - float(angulo_ideal_rad))
+                custo_ideal = peso_angulo_ideal * erro
+                for tipo in tipos:
+                    for g2, c in zip(graus_2c, custo_ideal):
+                        # respeita 'permitidos' se existir
+                        if permitidos is not None and (tipo.value, float(g2)) not in permitidos:
+                            continue
+                        custos_candidatos[(tipo.value, float(g2))] = float(c)
+                # retorna o conjunto original (em rad)
+                return tipos, angulos_rad.astype(float).tolist(), custos_candidatos
+
+            # garante LUTs base (idempotente); algumas avaliações usam offsets/escala
+            largura = float(contexto.get("Equipamento", {}).get("largura", 0.85))
+            L = float(contexto.get("Equipamento", {}).get("entre_eixos", 0.94))
+            self._ensure_luts(dados_matriz_custo, largura, L)
+
+            # ---- distância de simulação curta (penúltima linha, min 0.6 m)
+            velocidade = float(contexto.get("Carro", {}).get("Velocidade", 1.0))
+            distancia_sim_m_base = self._pick_sim_distance_from_lut(
+                v_mps=velocidade,
+                ms_left=(None if deadline is None else (deadline - time.perf_counter()) * 1000.0),
+                margem_ms=margem_ms,
+                default_if_missing=1.0
+            )
+
+            # ---- PRÉ-ORDENAÇÃO por erro ao ideal (barata)
+            erro = np.abs(angulos_rad - float(angulo_ideal_rad))
+            custo_ideal = peso_angulo_ideal * erro
+            ordem_global = np.argsort(custo_ideal)  # menor erro primeiro
+
+            # ---- orçamento dinâmico total e distribuição por tipo
+            tempo_util = max(0.0, ms_left() - margem_ms)
+            est = max(1.0, float(self._t_est_candidato_ms))
+            Kmax_total = min(12, ordem_global.size)
+            if deadline is None:
+                Kdyn_total = Kmax_total
+            else:
+                Kdyn_total = int(min(Kmax_total, max(3, tempo_util // est)))
+
+            # distribuição simples: round-robin/equitativa
+            T = max(1, len(tipos))
+            base = Kdyn_total // T
+            resto = Kdyn_total % T
+
+            # degrade a distância quando tempo aperta
+            escala_sim = 1.0
+            tl = ms_left()
+            if tl < 70.0:
+                escala_sim *= 0.8
+            if tl < 40.0:
+                escala_sim *= 0.6
+            distancia_sim_m = max(0.6, distancia_sim_m_base * escala_sim)
+
+            tipos_validos: list = []
+            angulos_validos: list = []
+            custos_candidatos: dict = {}
+            melhor_custo_local = float("inf")
+
+            # ---- laço por tipo com orçamento próprio
+            for i_tipo, tipo in enumerate(tipos):
+                if ms_left() <= margem_ms:
+                    break
+
+                # seleciona até K_per tipo respeitando 'permitidos' (se houver)
+                K_per = base + (1 if i_tipo < resto else 0)
+                if K_per <= 0:
+                    continue
+
+                cand_idx = []
+                if permitidos is None:
+                    # sem restrição: pega K_per primeiros da ordem global
+                    cand_idx = ordem_global[:K_per].tolist()
+                else:
+                    # respeita permitidos por (tipo, grau_2c)
+                    for idx in ordem_global:
+                        if len(cand_idx) >= K_per:
+                            break
+                        g2 = float(graus_2c[idx])
+                        if (tipo.value, g2) in permitidos:
+                            cand_idx.append(int(idx))
+
+                    # se não achou nada para este tipo, como fallback pegue primeiros K_per ignorando permitidos
+                    if not cand_idx:
+                        cand_idx = ordem_global[:K_per].tolist()
+
+                # avalia os candidatos deste tipo
+                for idx in cand_idx:
+                    if ms_left() <= margem_ms:
+                        break
+
+                    ang_rad = float(angulos_rad[idx])
+                    g2 = float(graus_2c[idx])
+                    c_ideal = float(custo_ideal[idx])
+
+                    # se houver 'permitidos', cheque antes de avaliar (em caso de fallback acima)
+                    if permitidos is not None and (tipo.value, g2) not in permitidos:
+                        continue
+
+                    t_c_ini = now()
+                    try:
+                        traj = self._simular_trajetoria_curta(p_atual, tipo, ang_rad, velocidade, distancia_sim_m)
+                        custo, valido = self._avaliar_trajetoria_matriz_custo(tipo, ang_rad, traj, p_ref, dados_matriz_custo)
+                        if valido:
+                            custo_total = float(custo) + float(c_ideal)
+
+                            # branch-and-bound leve sob pressão de tempo
+                            if (custo_total > melhor_custo_local * 1.20) and (ms_left() < 2 * self._t_est_candidato_ms):
+                                pass
+                            else:
+                                tipos_validos.append(tipo)
+                                angulos_validos.append(ang_rad)
+                                custos_candidatos[(tipo.value, g2)] = custo_total
+                                if custo_total < melhor_custo_local:
+                                    melhor_custo_local = custo_total
+                    except Exception as e:
+                        mostrar_log(f"Erro ao avaliar candidato {tipo.name} | {g2:.2f}°: {e}")
+                    finally:
+                        dt_ms = (now() - t_c_ini) * 1000.0
+                        # EMA do tempo por candidato
+                        alpha = float(self._ema_alpha)
+                        self._t_est_candidato_ms = (1.0 - alpha) * self._t_est_candidato_ms + alpha * dt_ms
+
+            # ---- fail-safe: se nada válido, devolve top heurístico (sem matriz)
+            if not tipos_validos:
                 custos_candidatos = {}
                 for tipo in tipos:
-                    for ang in angulos_rad:
-                        erro_ideal = abs(ang - angulo_ideal_rad)
-                        custo_ideal = peso_angulo_ideal * erro_ideal
-                        custos_candidatos[(tipo, ang)] = custo_ideal
-                return tipos, angulos_rad, custos_candidatos
+                    for idx in ordem_global[:max(3, Kdyn_total)]:
+                        g2 = float(graus_2c[idx])
+                        if permitidos is not None and (tipo.value, g2) not in permitidos:
+                            continue
+                        custos_candidatos[(tipo.value, g2)] = float(custo_ideal[idx])
+                # devolve ângulos (rad) correspondentes
+                ang_out = [float(angulos_rad[idx]) for idx in ordem_global[:max(3, Kdyn_total)]]
+                return tipos, ang_out, custos_candidatos
 
-            tipos_validos = []
-            angulos_validos = []
-            ctx_camera = contexto.get("VisualWorker", {}).get("Camera", {})
+            return tipos_validos, angulos_validos, custos_candidatos
 
-            linhas_info = analisar_linhas_por_profundidade(matriz_custo, "distancia_m", ctx_camera.get("FovH", 0.7))
-            linhas_ordenadas = sorted(linhas_info.values(), key=lambda item: item["distancia"])
-            distancia_min_m = linhas_ordenadas[0]["distancia"]
-            distancia_sim_m = np.clip(linhas_ordenadas[1]["distancia"], ctx_camera.get("DistanciaMin", 0.5), ctx_camera.get("DistanciaMax", 5.0))
-            largura = contexto.get("Equipamento", {}).get("largura", 0.85)
-            velocidade = contexto.get("Carro", {}).get("Velocidade", 1.0)
-
-            tarefas = []
-            custos_candidatos = {}
-            tipos_validos = set()
-            angulos_validos = set()
-
-            def avaliar(tipo, ang):
-                #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : Avaliando")
-                traj = self._simular_trajetoria_curta(p_atual, tipo, ang, velocidade, distancia_sim_m)
-                #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : pontos simulacao: {len(traj)}")
-                custo, valido = self._avaliar_trajetoria_matriz_custo(tipo, ang, traj, p_ref, matriz_custo, distancia_min_m, largura)
-                #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : valido {valido}, custo: {custo:.4f}")
-                if valido:
-                    erro_ideal = abs(ang - angulo_ideal_rad)
-                    custo_ideal = peso_angulo_ideal * erro_ideal
-                    return tipo, ang, custo + custo_ideal
-                return None
-
-            for tipo in tipos:
-                for ang in angulos_rad:
-                    tarefas.append(self.executor.submit(avaliar, tipo, ang))
-
-            try:
-                for fut in as_completed(tarefas, timeout=0.3):
-                    try:
-                        res = fut.result(timeout=0.05)
-                        if res:
-                            tipo, ang, custo = res
-                            tipos_validos.add(tipo)
-                            angulos_validos.add(ang)
-                            chave = (tipo.value, round(float(np.degrees(ang)), 2))
-                            custos_candidatos[chave] = custo
-                    except Exception as e:
-                        mostrar_log(f"⚠️ Timeout ou erro em future: {e}")
-            except Exception as e:
-                mostrar_log(f"Uma tarefa estourou timeout durante o processo de filtrar candidatos validos na matriz de custo: {e}")
-
-            return list(tipos_validos), list(angulos_validos), custos_candidatos
         except Exception as e:
             mostrar_log(f"❌ Erro ao filtrar candidatos por matriz: {e}")
             return [], [], {}
 
     def _simular_trajetoria_curta(self, p_atual, tipo, angulo, velocidade, dist_min):
+        """
+        Trajetória curta com v e ω constantes.
+        Compatível com o sistema: x += sin(θ)*d, y += cos(θ)*d.
+        Retorna lista [(x,y,θ)] de n amostras (n = ceil(dist_min / (v*dt))).
+        """
         try:
-            traj = []
-            distancia_m = velocidade * self.dt
-            if distancia_m <= 0.001:
+            dt = float(self.dt)
+            v  = float(velocidade)
+            if v <= 1e-6 or dist_min <= 1e-6 or dt <= 0.0:
                 return []
-            
-            omega = self.gps_handler.calcular_omega(velocidade, angulo, tipo)
 
-            x_temp, y_temp, theta_temp = p_atual[0], p_atual[1], p_atual[2]
+            dist_step = v * dt
+            n = int(math.ceil(dist_min / dist_step))
+            if n <= 0:
+                return []
+            n = min(n, int(getattr(self, "_traj_curta_max_steps", 64)))
 
-            passos = math.ceil(dist_min / distancia_m)
+            x0, y0, th0 = float(p_atual[0]), float(p_atual[1]), float(p_atual[2])
+            omega = float(self.gps_handler.calcular_omega(v, angulo, tipo))
 
-            for _ in range(passos):
-                x_temp, y_temp, theta_temp = self._nova_posicao(x_temp, y_temp, theta_temp, omega, velocidade, tipo, angulo)
-                traj.append((x_temp, y_temp, theta_temp))
+            k = np.arange(1, n + 1, dtype=np.float32)
+            if abs(omega) < 1e-6:
+                # reta: mesmo esquema do seu _nova_posicao
+                dx = dist_step * k * math.sin(th0)
+                dy = dist_step * k * math.cos(th0)
+                th = th0 + np.zeros_like(k, dtype=np.float32)
+                x = x0 + dx
+                y = y0 + dy
+            else:
+                R   = v / omega
+                dth = omega * dt
+                th  = th0 + dth * k
+                s0, c0 = math.sin(th0), math.cos(th0)
+                # solução fechada consistente com x+=sin(θ)*d, y+=cos(θ)*d
+                x = x0 + R * (c0 - np.cos(th))
+                y = y0 + R * (np.sin(th) - s0)
 
-            return traj
+            return list(zip(x.astype(float).tolist(),
+                            y.astype(float).tolist(),
+                            th.astype(float).tolist()))
         except Exception as e:
             mostrar_log(f"❌ Erro na simulação curta: {e}")
             return []
 
-    def _avaliar_trajetoria_matriz_custo(self, tipo, ang, trajetoria, p_ref, matriz_custo, distancia_min, largura_equipamento):
+    def _avaliar_trajetoria_matriz_custo(self, tipo, ang, trajetoria, p_ref, dados_matriz_custo):
         try:
-            if not matriz_custo or not trajetoria:
+            matriz_custo = dados_matriz_custo.get("Custo", None)
+            matriz_nav   = dados_matriz_custo.get("Navegavel", None)
+            if matriz_custo is None or matriz_nav is None or matriz_custo.size == 0 or not trajetoria:
                 return float("inf"), False
 
-            altura = len(matriz_custo)
-            largura = len(matriz_custo[0])
-            custo_total = 0.0
-            celulas_atravessadas = 0
+            # --- LUTs base (já preparados no _ensure_luts/_preparar_luts) ---
+            lut_y = self._lut["y2idx"]           # [Ny] mapeia y/discreto -> idx de linha (ordem reversa)
+            offsets = self._lut["offsets"]       # [H,2] (esq, dir) em colunas
+            escx    = self._lut["escala_x"]      # [H] escala (m/col) por linha
+            H, W = matriz_custo.shape
 
-            # Pré-processa as distâncias das linhas uma vez só
-            distancias_y = [float(linha[0]["distancia_m"]) for linha in matriz_custo][::-1]  # Agora do menor para maior!
+            # --- prefixos por frame (navegável e custo) ---
+            blocked = (~matriz_nav).astype(np.int32)    # 1 se bloqueado
+            pref_blk = np.cumsum(blocked, axis=1)       # [H,W]
+            pref_cst = np.cumsum(matriz_custo.astype(np.float32), axis=1)  # [H,W]
 
-            for x, y, theta in trajetoria:
-                x_rel, y_rel = self.gps_handler.converter_para_referencial_robo(x, y, p_ref[0], p_ref[1], p_ref[2])
-                if y_rel < distancia_min:
-                    #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : descartando, x: {x:.4f}, x_rel: {x_rel:.4f}, y: {y:.4f}, y_rel: {y_rel:.4f}, t: {theta:.4f}, d_min: {distancia_min:.4f}")
-                    continue
+            # --- trajetória em arrays ---
+            # trajetoria: lista de (x, y, theta). Só x,y importam aqui.
+            pts = np.asarray(trajetoria, dtype=np.float32)   # [N,3]
+            if pts.size == 0:
+                return float("inf"), False
+            xw = pts[:, 0]; yw = pts[:, 1]
 
-                # Busca binária na profundidade
-                idx = bisect_right(distancias_y, y_rel)
-                if idx >= altura:
-                    idx = altura - 1
-                if idx < 0:
-                    idx = 0
-                #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : x: {x:.4f}, x_rel: {x_rel:.4f}, y: {y:.4f}, y_rel: {y_rel:.4f}, t: {theta:.4f}, idx: {idx}, distancia: {(distancias_y[idx] if idx > -1 else 0):.4f}")
-                if idx < 0 or idx >= altura:
-                    continue  # fora da matriz
+            # --- transforma para referencial do robô (consistente com x+=sinθ*d, y+=cosθ*d) ---
+            xr, yr, thr = float(p_ref[0]), float(p_ref[1]), float(p_ref[2])
+            dx = xw - xr
+            dy = yw - yr
+            s0 = math.sin(thr)
+            c0 = math.cos(thr)
+            x_rel =  c0 * dx - s0 * dy
+            y_rel =  s0 * dx + c0 * dy
 
-                idx = altura - idx
+            # --- ignora pontos muito próximos (ex.: < 0.6 m) ---
+            # usa dist mínima do LUT (asc) se existir, senão 0.6
+            y0 = 0.6
+            dist_asc = self._lut.get("dist_asc", None)
+            if dist_asc is not None and len(dist_asc) > 0:
+                y0 = max(0.6, float(dist_asc[0]))
 
-                escala_x = matriz_custo[idx][0]["escala_x"]
-                largura_total = escala_x * largura
-                colunas_ocupadas = int(np.ceil(largura_equipamento / escala_x))
-                colunas_por_lado = colunas_ocupadas // 2
+            mask_far = y_rel >= y0
+            if not np.any(mask_far):
+                return float("inf"), False
 
-                # Se a matriz vai de -largura_total/2 (esquerda) até +largura_total/2 (direita)
-                j_central = int((x_rel + largura_total / 2) / escala_x)
+            x_rel = x_rel[mask_far]
+            y_rel = y_rel[mask_far]
 
-                for j in range(j_central - colunas_por_lado, j_central + colunas_por_lado + 1):
-                    if 0 <= j < largura:
-                        cel = matriz_custo[idx][j]
-                        celulas_atravessadas += 1
-                        custo_total += cel["custo"]
-                        if cel["indice_seg_chao"] < 0.3 or cel["indice_confiabilidade"] < 0.5:
-                            custo_total = custo_total / celulas_atravessadas
-                            #print(f"{tipo.name} | angulo: {np.degrees(ang):.2f} : Celula ruim atravessada: {cel}")
-                            return custo_total, False  # 🚫 Invalida aqui já
-            custo_total = custo_total / max(1, celulas_atravessadas)
-            return custo_total, True
+            # --- mapeia y_rel -> idx de linha via LUT ---
+            ky  = np.clip((y_rel / self.dy).astype(np.int32), 0, lut_y.size - 1)
+            idx = lut_y[ky]                          # [M] índices de linha na ordem da matriz (reversa)
+
+            # --- coluna central por ponto ---
+            sx   = escx[idx]                         # [M] escala por linha
+            larg = (W * sx) * 0.5                    # [M] metade da largura em metros
+            j_c  = np.floor((x_rel + larg) / sx).astype(np.int32)         # [M]
+            offL = offsets[idx, 0]
+            offR = offsets[idx, 1]
+            j0   = (j_c + offL).clip(0, W - 1)
+            j1   = (j_c + offR).clip(0, W - 1)
+            # garante j0<=j1
+            j0, j1 = np.minimum(j0, j1), np.maximum(j0, j1)
+
+            # --- checagem de navegabilidade via prefixo ---
+            j0m1 = (j0 - 1).clip(-1, W - 1)
+            left_blk  = np.where(j0m1 >= 0, pref_blk[idx, j0m1], 0)
+            right_blk = pref_blk[idx, j1]
+            blocked_any = (right_blk - left_blk) > 0    # [M] True se há bloqueio na fatia
+
+            # --- soma de custos via prefixo (só nas válidas) ---
+            left_cst  = np.where(j0m1 >= 0, pref_cst[idx, j0m1], 0.0)
+            right_cst = pref_cst[idx, j1]
+            seg_sum   = (right_cst - left_cst).astype(np.float32)
+            seg_len   = (j1 - j0 + 1).astype(np.int32)
+
+            valid_mask = ~blocked_any
+            if not np.any(valid_mask):
+                # alguma fatia bloqueou e nenhuma válida sobrou
+                return 0.0, False
+
+            custo_total = float(np.sum(seg_sum[valid_mask]))
+            cells       = int(np.sum(seg_len[valid_mask]))
+
+            # se houve qualquer bloqueio em algum ponto da trajetória, sinaliza inválido (mantém custo parcial médio)
+            invalido = bool(np.any(blocked_any))
+
+            if cells <= 0:
+                return float("inf"), False
+
+            return (custo_total / cells), (not invalido)
 
         except Exception as e:
             mostrar_log(f"❌ Erro ao avaliar trajetoria na matriz: {e}")
             return float("inf"), False
 
-    def _uniformizar_angulos_candidatos(self, angulos_validos, angulo_ideal_rad, max_candidatos=9):
+    def _uniformizar_angulos_candidatos(self, angulos_validos, angulo_ideal_rad, N=9):
+        """
+        Reduz a lista de ângulos (rad) para no máximo N, preservando:
+        - o ângulo mais próximo do ideal,
+        - extremos (±angulo_max) se existirem no conjunto,
+        - 0.0 se existir no conjunto,
+        e preenchendo o restante com amostragem uniforme da faixa.
+        Usa tolerância para comparar floats e evita clusters muito próximos.
+        """
         try:
-            if len(angulos_validos) <= max_candidatos:
-                return angulos_validos
+            if not angulos_validos:
+                return []
 
-            extremos = []
-            rad_min = -np.radians(self.angulo_max_graus)
-            rad_max = np.radians(self.angulo_max_graus)
+            # --- setup / limites
+            rad_max = float(np.radians(self.angulo_max_graus))
+            rad_min = -rad_max
+            eps = float(np.deg2rad(0.05))     # ~0.05°: tolerância para igualdade
+            # separação mínima desejada entre ângulos selecionados
+            # (meio grau ou espaçamento uniforme aproximado)
+            min_sep = max(float(np.deg2rad(0.5)), (rad_max - rad_min) / max(1, N - 1))
 
-            if rad_min in angulos_validos:
-                extremos.append(rad_min)
-            if 0.0 in angulos_validos:
-                extremos.append(0.0)
-            if rad_max in angulos_validos:
-                extremos.append(rad_max)
-            if angulo_ideal_rad in angulos_validos and angulo_ideal_rad not in extremos:
-                extremos.append(angulo_ideal_rad)
+            # array, limpa NaN, clampa
+            a = np.asarray(angulos_validos, dtype=np.float32)
+            a = a[np.isfinite(a)]
+            if a.size == 0:
+                return []
 
-            restantes = [a for a in angulos_validos if a not in extremos]
-            restantes.sort()
+            a = np.clip(a, rad_min, rad_max)
+            a.sort()
 
-            qtd_restantes = max_candidatos - len(extremos)
-            if qtd_restantes <= 0:
-                return sorted(extremos[:max_candidatos])
+            # --- de-dup com tolerância
+            uniq = [float(a[0])]
+            for v in a[1:]:
+                if abs(float(v) - uniq[-1]) > eps:
+                    uniq.append(float(v))
+            a = np.array(uniq, dtype=np.float32)
 
-            # Distribuição uniforme usando linspace para evitar perdas
-            indices = np.linspace(0, len(restantes) - 1, qtd_restantes).astype(int)
-            selecionados = [restantes[i] for i in indices]
+            if a.size <= N:
+                return a.astype(float).tolist()
 
-            #print(f"Candidatos uniformizados de {len(angulos_validos)} para {len(extremos + selecionados)}")
+            # --- sempre incluir o mais próximo do ideal
+            idx_ideal = int(np.argmin(np.abs(a - float(angulo_ideal_rad))))
+            sel_idx = {idx_ideal}
 
-            return sorted(set(extremos + selecionados))
+            # --- incluir extremos se EXISTIREM no conjunto (com tolerância)
+            if abs(a[0] - rad_min) <= 2*eps:
+                sel_idx.add(0)
+            if abs(a[-1] - rad_max) <= 2*eps:
+                sel_idx.add(a.size - 1)
+
+            # --- incluir 0.0 se existir no conjunto
+            zero_hits = np.where(np.abs(a) <= 2*eps)[0]
+            if zero_hits.size > 0:
+                sel_idx.add(int(zero_hits[0]))
+
+            # --- preencher o restante por amostragem estratificada uniforme
+            k_rest = max(0, N - len(sel_idx))
+            if k_rest > 0:
+                mask_rest = np.ones(a.shape[0], dtype=bool)
+                mask_rest[list(sel_idx)] = False
+                rest = a[mask_rest]
+                if rest.size > 0:
+                    # índices uniformes na parte restante
+                    idx_lin = np.linspace(0, rest.size - 1, k_rest).astype(int)
+                    # mapeia de volta para índices globais
+                    rest_idx_global = np.where(mask_rest)[0][idx_lin]
+                    sel_idx.update(int(i) for i in rest_idx_global)
+
+            # --- aplica separação mínima (greedy) e completa se faltar
+            sel = sorted([float(a[i]) for i in sorted(sel_idx)])
+            # filtra por min_sep
+            filtered = []
+            for v in sel:
+                if not filtered or abs(v - filtered[-1]) >= (min_sep - 1e-12):
+                    filtered.append(v)
+
+            # se caiu demais por causa do espaçamento, completa com candidatos mais distantes do set atual
+            if len(filtered) < N:
+                need = N - len(filtered)
+                # candidatos não escolhidos
+                chosen_mask = np.zeros(a.shape[0], dtype=bool)
+                # marca os que já estão em filtered (com tolerância)
+                for v in filtered:
+                    chosen_mask |= (np.abs(a - v) <= eps)
+
+                cand = a[~chosen_mask]
+                if cand.size > 0:
+                    # distância para o conjunto 'filtered'
+                    ref = np.array(filtered, dtype=np.float32)
+                    dmin = np.min(np.abs(cand[:, None] - ref[None, :]), axis=1)
+                    ord_idx = np.argsort(-dmin)  # pega os mais distantes primeiro
+                    for idx in ord_idx[:need]:
+                        filtered.append(float(cand[int(idx)]))
+                        if len(filtered) >= N:
+                            break
+                    filtered.sort()
+
+            # corte final caso tenha passado
+            if len(filtered) > N:
+                pick = np.linspace(0, len(filtered) - 1, N).astype(int)
+                filtered = [filtered[i] for i in pick]
+
+            return filtered
 
         except Exception as e:
             mostrar_log(f"❌ Erro ao uniformizar ângulos: {e}")
-            return angulos_validos
+            return list(angulos_validos)
 
     def _selecionar_melhores(self, candidatos: list, N: int = 10) -> list:
+        """
+        Deduplica por (tipo, ângulo) e retorna os N de menor custo.
+        - Se custo for NaN/inf, é descartado.
+        - Usa o último comando do candidato como par (tipo, angulo).
+        """
         try:
             if not candidatos:
                 return []
 
-            # Ordena pelo custo total (menor primeiro)
-            candidatos_ordenados = sorted(candidatos, key=lambda c: c.get("custo", float('inf')))
+            def custo_ok(c):
+                v = float(c.get("custo", float("inf")))
+                return v if math.isfinite(v) else float("inf")
+
+            # 1) Dedup por (tipo, ângulo° com 2 casas), mantendo menor custo
+            best_by_key = {}
+            for c in candidatos:
+                cmds = c.get("comandos") or []
+                if not cmds:
+                    continue
+                tipo, ang_rad = cmds[-1]
+                try:
+                    tipo_val = getattr(tipo, "value", tipo)
+                    ang_deg2 = round(float(np.degrees(float(ang_rad))), 2)
+                except Exception:
+                    continue
+
+                k = (tipo_val, ang_deg2)
+                cc = custo_ok(c)
+                if not math.isfinite(cc):
+                    continue
+                prev = best_by_key.get(k)
+                if prev is None or cc < custo_ok(prev):
+                    best_by_key[k] = c
+
+            if not best_by_key:
+                return []
+
+            # 2) Pega os N menores por custo (sem ordenar toda a lista)
+            pool = list(best_by_key.values())
+            topN = heapq.nsmallest(min(N, len(pool)), pool, key=custo_ok)
+            return topN
 
-            # Retorna os N melhores
-            return candidatos_ordenados[:N]
         except Exception as e:
             mostrar_log(f"❌ Erro ao selecionar melhores candidatos: {e}")
             return []
 
-    def _filtrar_por_margem_angular(self, candidatos, margem_graus=2.0):
-        """Mantém apenas o melhor candidato em cada margem angular."""
-        selecionados = []
-        angulos_escolhidos = []
-        for c in candidatos:
-            # Pega o último ângulo do comando
-            if not c["comandos"]:
-                continue
-            _, angulo = c["comandos"][-1]
-            # Se não tem nada próximo, adiciona
-            if not any(abs(np.degrees(angulo) - np.degrees(a)) < margem_graus for a in angulos_escolhidos):
-                selecionados.append(c)
-                angulos_escolhidos.append(angulo)
-        return selecionados
+    def _filtrar_por_margem_angular(self, candidatos: list, margem_graus: float = 2.0, por_tipo: bool = False) -> list:
+        """
+        Mantém diversidade angular: varre em ordem de menor custo e descarta
+        candidatos cujo último ângulo (em graus) estiver a < margem_graus de algum já escolhido.
+        - Se por_tipo=True, aplica a margem separadamente por tipo.
+        """
+        try:
+            if not candidatos:
+                return []
 
+            def custo_ok(c):
+                v = float(c.get("custo", float("inf")))
+                return v if math.isfinite(v) else float("inf")
+
+            # ordena por custo crescente (garante que o melhor entra em cada “cluster” angular)
+            cand_ord = sorted(
+                (c for c in candidatos if (c.get("comandos") or [])),
+                key=custo_ok
+            )
+
+            selecionados = []
+            # guardamos ângulos já escolhidos (em graus) — por tipo se por_tipo=True
+            escolhidos = {} if por_tipo else {"_": []}
+
+            for c in cand_ord:
+                tipo, ang_rad = c["comandos"][-1]
+                tipo_key = getattr(tipo, "value", tipo) if por_tipo else "_"
+                ang_deg = float(np.degrees(float(ang_rad)))
+
+                lista = escolhidos.setdefault(tipo_key, [])
+                # aceita se não há ângulo escolhido “muito perto”
+                if all(abs(ang_deg - a) >= margem_graus for a in lista):
+                    selecionados.append(c)
+                    lista.append(ang_deg)
+
+            return selecionados
+
+        except Exception as e:
+            mostrar_log(f"❌ Erro ao filtrar por margem angular: {e}")
+            return candidatos
+    
     def _simular_passo(self, x, y, theta, tipo, angulo_testado, custos_candidatos, comando_anterior, contexto, visitados):
         # Custo pode variar de 0 ~ 13, em situacoes mais extremas, por passo
         try:
@@ -1566,8 +1416,9 @@ class ControladorMPC:
             self.x_ant, self.y_ant = 0.0, 0.0
             chave = (tipo.value, round(float(np.degrees(angulo_testado)), 2))
             simulacoes = []
-            comando_anterior_copy = deepcopy(comando_anterior)
-            pontos_visitados = deepcopy(visitados)
+            prev_ang  = comando_anterior.get("angulo", 0.0)
+            prev_tipo = comando_anterior.get("tipo", TipoMovimentoDirecional.RodasDianteiras.value)
+            pontos_visitados = visitados.copy()
             velocidade = contexto.get("Carro", {}).get("Velocidade", 0.0)
             angulo_caminho = np.radians(contexto.get("Carro", {}).get("AnguloCaminho", 0.0))
             status_carro = StatusCarroMapa(contexto.get("Carro", {}).get("Status", StatusCarroMapa.Parado.value))
@@ -1591,7 +1442,7 @@ class ControladorMPC:
 
                     erro_pos = np.linalg.norm([x_sim - ponto_alvo_sim[0], y_sim - ponto_alvo_sim[1]])
                     peso_erro_orientacao_caminho = 0.3 if status_carro == StatusCarroMapa.CaminhandoRua and dentro_corredor else 0.7
-                    erro_ori_sim = self._erro_angular(orient_sim, theta_sim, angulo_caminho, peso_erro_orientacao_caminho)
+                    erro_ori_sim = self.gps_handler.erro_angular(orient_sim, theta_sim, angulo_caminho, peso_erro_orientacao_caminho)
 
                     pesos = self._calcular_pesos_movimento(contexto, erro_ori_sim)
                     peso_erro_pos, peso_erro_ori, peso_suavidade, peso_fator_re, peso_movimento = pesos[0], pesos[1], pesos[2], pesos[3], pesos[5]
@@ -1600,7 +1451,7 @@ class ControladorMPC:
                     vetor_alvo_norm = vetor_alvo / np.linalg.norm(vetor_alvo)
                     vetor_movel = np.array([np.cos(theta_sim), np.sin(theta_sim)])
                     cos_angulo = -np.dot(vetor_movel, vetor_alvo_norm)
-                    delta_angulo = abs(angulo_testado - comando_anterior_copy.get("angulo", 0))
+                    delta_angulo = abs(angulo_testado - prev_ang)
 
                     custo_pos = erro_pos * peso_erro_pos
                     custo_ori = ((erro_ori_sim / np.pi) * erro_pos) * peso_erro_ori
@@ -1612,8 +1463,8 @@ class ControladorMPC:
 
                     custo_total += custo_mapa
 
-                    comando_anterior_copy["angulo"] = angulo_testado
-                    comando_anterior_copy["tipo"] = tipo
+                    prev_ang = angulo_testado
+                    prev_tipo = tipo
 
                     #if matriz_concluida:
                     #    break
@@ -1637,35 +1488,320 @@ class ControladorMPC:
         y_sim = y + (np.cos(theta_sim) * distancia_m)
 
         return x_sim, y_sim, theta_sim
-    
-    def _nova_posicao_new(self, x, y, theta, omega, velocidade, tipo, angulo_rad):
-        dt = self.dt
-        v = velocidade
-        distancia = v * dt
 
-        if tipo == TipoMovimentoDirecional.MovimentoDiagonal:
-            # crab: yaw não muda; desloca na direção (theta + steer)
-            move_heading = theta + angulo_rad
-            theta_sim = theta
-            dx = distancia * np.sin(move_heading)  # Leste
-            dy = distancia * np.cos(move_heading)  # Norte
-            x_sim = x + dx
-            y_sim = y + dy
-            return x_sim, y_sim, theta_sim
+    def _prefiltrar_angulos_por_matriz_quick(
+        self,
+        angulos_deg,          # iterable de graus
+        p_atual,              # (x, y, theta) do candidato atual
+        p_ref,                # (x,y,theta) referência (pode não usar aqui)
+        dados_matriz_custo,   # dict com Custo/Navegavel/EscalasX/DistanciasRef
+        largura_equip,        # metros
+        max_linhas=3,         # quantas linhas à frente checar (as mais distantes)
+        deadline=None, margem_ms=5.0
+    ):
+        """
+        Retorna um np.ndarray(bool) mask do mesmo tamanho de angulos_deg indicando
+        quais ângulos passam no gate rápido da matriz (todas as linhas checadas navegáveis).
+        """
+        # Se não tem matriz, tudo liberado
+        custo = dados_matriz_custo.get("Custo", None)
+        nav   = dados_matriz_custo.get("Navegavel", None)
+        if custo is None or nav is None or nav.size == 0:
+            a = np.asarray(angulos_deg, dtype=np.float32)
+            return np.ones(a.shape, dtype=bool)
 
-        # Demais modos: pode ter guinada
-        if abs(omega) < 1e-6:
-            # reta
-            theta_sim = theta
-            dx = distancia * np.cos(theta_sim)
-            dy = distancia * np.sin(theta_sim)
-            x_sim = x - dx
-            y_sim = y - dy
-            return x_sim, y_sim, theta_sim
+        now = time.perf_counter
+        def time_ok():
+            return True if deadline is None else (deadline - now())*1000.0 > margem_ms
+
+        # LUTs
+        lut_y  = self._lut["y2idx"]
+        offs   = self._lut["offsets"]          # shape: [H, 2]
+        escx   = self._lut["escala_x"]         # shape: [H]
+        H, W   = nav.shape
+
+        # linhas a checar: pegue as últimas (mais “pra frente”)
+        dist_ref = np.asarray(dados_matriz_custo.get("DistanciasRef", []), dtype=np.float32)
+        if dist_ref.size == 0:
+            a = np.asarray(angulos_deg, dtype=np.float32)
+            return np.ones(a.shape, dtype=bool)
+
+        # usamos as linhas com maiores distâncias (últimas do array, assumindo crescente)
+        linhas = np.arange(max(0, dist_ref.size - max_linhas), dist_ref.size, dtype=int)
+
+        # converte angulos pra rad e calcula tan (uma vez)
+        a_deg = np.asarray(angulos_deg, dtype=np.float32)
+        a_rad = np.deg2rad(a_deg)
+        tan_a = np.tan(a_rad)
+
+        # posição atual do robô no referencial do robô é (0,0,theta=0).
+        # A projeção do centro à distância y: x ≈ y * tan(a)
+        # Para cada linha selecionada, fazemos o gate de footprint.
+        mask = np.ones(a_deg.shape, dtype=bool)
+
+        for li in linhas:
+            if not time_ok():
+                break
+
+            # distância y desta linha (em metros)
+            y = float(dist_ref[li])
+            if y <= 0.0:
+                continue
+
+            # coluna central para cada ângulo
+            # j_c = int((x_rel + larg_total/2) / escala_x), onde x_rel ≈ y * tan(a)
+            x_rel = y * tan_a
+            idx   = int(lut_y[min(int(y / getattr(self, "dy", 0.02)), lut_y.size - 1)])  # fallback por segurança
+            sx    = float(escx[idx])
+            larg_total = sx * W
+            j_c = np.floor((x_rel + larg_total * 0.5) / sx).astype(np.int32)
+
+            # fatia footprint na coluna
+            offL, offR = offs[idx]
+            j0 = np.clip(j_c + offL, 0, W - 1)
+            j1 = np.clip(j_c + offR, 0, W - 1)
+
+            # checa navegabilidade por ângulo (tudo True na fatia)
+            # (fazemos com loop leve sobre ângulos para evitar broadcasting 2D grande; A << W)
+            ok = []
+            row_nav = nav[idx]  # 1D view
+            for c0, c1 in zip(j0, j1):
+                if c0 > c1:
+                    c0, c1 = c1, c0
+                ok.append(bool(row_nav[c0:c1+1].all()))
+            mask &= np.array(ok, dtype=bool)
+
+            # early-exit: se já tudo falso, pode sair
+            if not mask.any():
+                break
+
+        return mask
+
+    def _quick_gate_by_lut_tipo(self, angulos_deg, dados_matriz_custo, tipo, max_linhas=3):
+        """
+        Gate rápido por LUT:
+        - retorna mask (K,) indicando se o ângulo passa em TODAS as 'max_linhas' finais,
+        - retorna também avg_cost (K,) best-effort p/ ranking rápido (média das fatias).
+        Usa indexação segura (take_along_axis) para evitar shape mismatch.
+        """
+        try:
+            lut = getattr(self, "_lut_tipo", {}).get(tipo.value)
+            if not lut:
+                # sem LUT do tipo => não barra nada
+                K = len(angulos_deg)
+                return np.ones(K, dtype=bool), np.full(K, np.inf, dtype=np.float32)
+
+            C = dados_matriz_custo.get("Custo", None)
+            NAV = dados_matriz_custo.get("Navegavel", None)
+            if C is None or NAV is None or C.size == 0:
+                K = len(angulos_deg)
+                return np.ones(K, dtype=bool), np.full(K, np.inf, dtype=np.float32)
+
+            C = np.asarray(C, dtype=np.float32)
+            NAV = np.asarray(NAV, dtype=bool)
+            H, W = C.shape
+
+            # linhas a verificar (últimas max_linhas)
+            R0 = max(0, H - int(max_linhas))
+            rows = np.arange(R0, H, dtype=np.int32)              # (R,)
+            R = rows.size
+
+            # mapeia ângulos -> índices na grade do LUT (K,)
+            K = len(angulos_deg)
+            k_idx = self._map_ang_to_idx_fast(angulos_deg, lut).astype(np.int32)  # (K,)
+
+            # pega j0/j1 como matrizes (R,K) usando np.ix_, sem conflito
+            j0_tab = lut["j0"]    # (H,Kgrid)
+            j1_tab = lut["j1"]    # (H,Kgrid)
+            j0 = j0_tab[np.ix_(rows, k_idx)]                     # (R,K)
+            j1 = j1_tab[np.ix_(rows, k_idx)]                     # (R,K)
+
+            # prefixos por linha (cacheáveis, mas simples aqui)
+            blocked = (~NAV).astype(np.int32)
+            pref_blk_rows = np.cumsum(blocked[rows, :], axis=1)  # (R,W)
+            pref_cst_rows = np.cumsum(C[rows, :], axis=1)        # (R,W)
+
+            # limites e comprimentos
+            j0m1 = np.clip(j0 - 1, -1, W - 1)                    # (R,K)
+            # take_along_axis exige índices >=0; tratamos -1 com 'where'
+            left_blk  = np.where(j0m1 >= 0,
+                                np.take_along_axis(pref_blk_rows, j0m1, axis=1),
+                                0)
+            right_blk = np.take_along_axis(pref_blk_rows, j1, axis=1)
+            blocked_any = (right_blk - left_blk) > 0             # (R,K)
+
+            left_cst  = np.where(j0m1 >= 0,
+                                np.take_along_axis(pref_cst_rows, j0m1, axis=1),
+                                0.0)
+            right_cst = np.take_along_axis(pref_cst_rows, j1, axis=1)
+            seg_sum   = (right_cst - left_cst).astype(np.float32)  # (R,K)
+            seg_len   = (j1 - j0 + 1).astype(np.float32)           # (R,K)
+            seg_len   = np.maximum(seg_len, 1.0)
+
+            # ângulo passa se NENHUMA das R linhas tem bloqueio
+            mask = ~np.any(blocked_any, axis=0)                  # (K,)
+            # custo médio nas linhas válidas (só p/ ranking rápido)
+            avg_cost = np.mean(seg_sum / seg_len, axis=0)        # (K,)
+
+            return mask, avg_cost
+
+        except Exception as e:
+            mostrar_log(f"⚠️ quick_gate falhou para {tipo}: {e}")
+            K = len(angulos_deg)
+            return np.ones(K, dtype=bool), np.full(K, np.inf, dtype=np.float32)
+
+    def _pick_sim_distance_from_lut(self, v_mps, ms_left, margem_ms, default_if_missing=1.0):
+        # distâncias do LUT; se não houver, cai pro default
+        dist_asc = getattr(self, "_lut", {}).get("dist_asc", None)
+        if dist_asc is None or len(dist_asc) == 0:
+            return float(default_if_missing)
+
+        dist_asc = np.asarray(dist_asc, dtype=np.float32)
+        d_min = 0.6                           # não simular “micro” passos
+        d_max = float(dist_asc[-1])           # limite do costmap para frente
+
+        # horizonte temporal-alvo para um passo curto de simulação
+        # usa o tempo que sobra no ciclo (menos a margem) e um clamp razoável
+        if ms_left is None:
+            t_hz = getattr(self, "tempo_execucao_local", 0.5)
+            tau = float(np.clip(t_hz, 0.15, 0.60))
         else:
-            # arco (uniciclo, 0=N, +θ horário)
-            dtheta = omega * dt
-            x_sim = x - (v / omega) * (np.cos(theta + dtheta) - np.cos(theta))
-            y_sim = y - (v / omega) * (-np.sin(theta + dtheta) + np.sin(theta))
-            theta_sim = theta + dtheta
-            return x_sim, y_sim, theta_sim
+            tau = float(np.clip((ms_left - margem_ms) / 1000.0, 0.15, 0.60))
+
+        D = float(np.clip(v_mps * tau, d_min, d_max))
+
+        # “snap” para a linha mais próxima **sem ser a última** (evita borda)
+        idx = int(np.searchsorted(dist_asc, D, side="left"))
+        idx = max(0, min(idx, len(dist_asc) - 2))   # evita usar o último índice
+        return float(dist_asc[idx])
+
+
+    def _dbg_cost_rows_and_cols(self, dados_matriz_custo, tipo, angulos_deg, max_linhas=3):
+        """
+        Retorna (rows, j0, j1) para desenhar footprints por ângulo.
+        rows:  (R,) linhas escolhidas (últimas R)
+        j0/j1: (R,K) colunas esquerda/direita por (linha, ângulo)
+        """
+        C = dados_matriz_custo.get("Custo")
+        if C is None or C.size == 0:
+            return None, None, None
+        H, W = C.shape
+        R0 = max(0, H - int(max_linhas))
+        rows = np.arange(R0, H, dtype=np.int32)  # (R,)
+        lut = getattr(self, "_lut_tipo", {}).get(tipo.value)
+        if not lut:
+            return rows, None, None
+        k_idx = self._map_ang_to_idx_fast(angulos_deg, lut).astype(np.int32)  # (K,)
+        j0 = lut["j0"][np.ix_(rows, k_idx)]   # (R,K)
+        j1 = lut["j1"][np.ix_(rows, k_idx)]   # (R,K)
+        return rows, j0, j1
+
+    def debug_plot_matriz_custo(
+        self, dados_matriz_custo, tipos, angulos_deg, *,
+        max_linhas=3, mostra_nav=True, titulo="debug_cost.png", highlight=None
+    ):
+        """
+        Gera um PNG com as últimas 'max_linhas' do costmap e footprints dos ângulos para cada tipo.
+        - tipos: lista de TipoMovimentoDirecional (ex.: [RodasDianteiras, MovimentoArco])
+        - angulos_deg: lista de ângulos em graus (os candidatos após gate/ranking)
+        - highlight: tuple (tipo.value, grau_2c) para destacar o ângulo escolhido
+        """
+        try:
+            import matplotlib.pyplot as plt
+            C = dados_matriz_custo.get("Custo"); NAV = dados_matriz_custo.get("Navegavel")
+            if C is None or C.size == 0:
+                return
+            C = np.asarray(C, dtype=np.float32)
+            H, W = C.shape
+
+            angulos_deg = list(angulos_deg or [])
+            if len(angulos_deg) == 0:
+                return
+
+            # linhas que vamos mostrar (as últimas max_linhas)
+            rows = np.arange(max(0, H - int(max_linhas)), H, dtype=np.int32)
+            R = rows.size
+
+            # normalização simples do custo pra visualizar
+            cmin, cmax = float(np.percentile(C, 5)), float(np.percentile(C, 95))
+            cmax = max(cmax, cmin + 1e-6)
+
+            # pré-calcula j0/j1 por tipo (para TODAS as linhas/ângulos de uma vez)
+            jmaps = {}  # tipo.value -> (j0(R,K), j1(R,K))
+            for t in tipos:
+                lut = getattr(self, "_lut_tipo", {}).get(t.value)
+                if not lut:
+                    continue
+                k_idx = self._map_ang_to_idx_fast(angulos_deg, lut).astype(np.int32)  # (K,)
+                j0 = lut["j0"][np.ix_(rows, k_idx)]  # (R,K)
+                j1 = lut["j1"][np.ix_(rows, k_idx)]  # (R,K)
+                jmaps[t.value] = (j0, j1)
+
+            # se nenhum tipo tem LUT, não há o que desenhar de footprint
+            if not jmaps:
+                return
+
+            fig, axes = plt.subplots(1, R, figsize=(4*R, 4), squeeze=False)
+            axes = axes[0]
+
+            dist_asc = getattr(self, "_lut", {}).get("dist_asc", None)
+
+            for i, r in enumerate(rows):
+                ax = axes[i]
+                ax.set_title(f"linha {int(r)}")
+
+                # distância aproximada dessa linha (se disponível)
+                dtxt = ""
+                if dist_asc is not None and len(dist_asc) == H:
+                    # rows são os últimos índices; em nosso LUT, índice maior = mais perto
+                    dtxt = f"  (≈ {dist_asc[r]:.2f} m)"
+
+                ax.set_title(f"linha {int(r)}{dtxt}")
+
+                row_img = ((C[r] - cmin) / (cmax - cmin + 1e-9))[None, :]  # (1, W)
+                ax.imshow(row_img, aspect='auto', origin='upper', vmin=0.0, vmax=1.0)
+
+                ax.set_xlim(0, W-1)
+                ax.set_ylim(1, 0)
+                ax.set_yticks([]); ax.set_xticks([])
+
+                y_pix = 0.5
+
+                # coluna central (eixo do robô)
+                ax.vlines(W/2, 0.1, 0.9, colors='k', linestyles='dotted', linewidth=1.5, alpha=0.8)
+
+                # NAV (x onde não é navegável)
+                if mostra_nav and NAV is not None:
+                    nav_r = np.asarray(NAV[r], dtype=bool)
+                    bad_cols = np.where(~nav_r)[0]
+                    if bad_cols.size > 0:
+                        ax.scatter(bad_cols, np.full_like(bad_cols, y_pix, dtype=float), s=12, marker='x')
+
+                # footprints por tipo e ângulo
+                desenhou_algo = False
+                for t in tipos:
+                    pair = jmaps.get(t.value)
+                    if pair is None:
+                        continue
+                    j0_mat, j1_mat = pair  # (R,K)
+                    for k, g in enumerate(angulos_deg):
+                        j0k = int(j0_mat[i, k]); j1k = int(j1_mat[i, k])
+                        color = 'w'
+                        if highlight is not None:
+                            tv, gg = highlight
+                            if int(getattr(t, "value", t)) == int(tv) and abs(float(g) - float(gg)) < 1e-6:
+                                color = 'r'
+                        ax.hlines(y_pix, j0k, j1k, colors=color, linewidth=4, alpha=0.95)
+                        # marca o centro do footprint
+                        jc = (j0k + j1k) // 2
+                        ax.plot(jc, y_pix, marker='o', markersize=3.5, color=color, alpha=0.95)
+                        desenhou_algo = True
+
+            fig.suptitle(titulo)
+            fig.tight_layout()
+            fig.savefig(titulo, dpi=110)
+            plt.close(fig)
+
+        except Exception as e:
+            mostrar_log(f"[DEBUG] falha no debug_plot_matriz_custo: {e}")
+
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/_4_em_andamento.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/_4_em_andamento.py
index a099a140d..6c07fd87b 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/_4_em_andamento.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/_4_em_andamento.py
@@ -23,13 +23,14 @@ class ProcessadorEmAndamento(ProcessadorBase):
 
     def processar(self):
         try:
-            angulo_sp, tipo_dir, simulacao, parada_necessaria = definir_comando_dir(self.pid_dir)
-            velocidade_sp = definir_comando_mov(parada_necessaria, self.filtro_mov)
+            angulo_sp, tipo_dir, simulacao, parada_necessaria, erro = definir_comando_dir(self.pid_dir)
+            velocidade_sp = definir_comando_mov(parada_necessaria, erro, self.filtro_mov)
             return comando_controle(
                 percentual_velocidade=velocidade_sp, 
                 angulo=angulo_sp, 
                 tipo_movimento=tipo_dir, 
-                simulacao=simulacao
+                simulacao=simulacao, 
+                erro=erro
             )
         except Exception as e:
             print(f"Erro no processador EmAndamento: {e}")
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/__pycache__/_4_em_andamento.cpython-311.pyc b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/__pycache__/_4_em_andamento.cpython-311.pyc
index 2842061ede3cac2e61b53d44a4191daa0ce6ffd2..466492cb2d53d1ee6769436030aaf332817c8d32 100644
GIT binary patch
delta 521
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delta 494
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/__pycache__/padroes.cpython-311.pyc b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/__pycache__/padroes.cpython-311.pyc
index 511b60d42a1f14b042a7dd606d357f1e51c6fcd4..166ec9452c4030b8346ba318461a15a4cd080d25 100644
GIT binary patch
delta 747
zcmZ8eO=uHA6rS1L$<AhzR@!LVkkD>_jfED4Vh>fU!O(*iY*A<t>$bBcV3M_yv})Oa
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YhtUQdOy6p9Zn?q#!v!&9j%ae=UlG!$<p2Nx

delta 677
zcmYjO&rcIU6rS0g+3s$CAYIxjt+Y*xv|gwt1P`qVF&I7A1I7brtV?HWO-i?!79<1`
z4;(ns3<pd+a6*mVym;`Se}Qs?CgQ=9;nK*Z@nC#YZ1g4Xd*6He?VI;zb|S~&iI;xA
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/padroes.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/padroes.py
index 9a761a0e8..97ece6021 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/padroes.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/manager_worker/processadores/padroes.py
@@ -3,13 +3,14 @@ from shared.enums import ModoOperacao, StatusOperacao
 from manager_worker.config import mostrar_log
 from shared.contexto_global_redis import ContextoGlobalRedis, CtxKey
 
-def comando_controle(percentual_velocidade: float, angulo: float, tipo_movimento: int, simulacao = []):
+def comando_controle(percentual_velocidade: float, angulo: float, tipo_movimento: int, simulacao = [], erro: bool = False):
     hb_atual = ContextoGlobalRedis.get_controle().get("heartbeat", 0)
-    try:
-        hb_atual = int(hb_atual) % 10
-    except:
-        hb_atual = 0
-    heartbeat = (hb_atual + 1) % 10
+    if erro == False:
+        try:
+            hb_atual = int(hb_atual) % 10
+        except:
+            hb_atual = 0
+        hb_atual = (hb_atual + 1) % 10
 
     ContextoGlobalRedis.atualizar_ctx_dict(
         CtxKey.DadosControle, 
@@ -17,14 +18,14 @@ def comando_controle(percentual_velocidade: float, angulo: float, tipo_movimento
         tipo_movimento_direcional=tipo_movimento,
         velocidade_sp=percentual_velocidade,
         simulacao=simulacao,
-        heartbeat=heartbeat
+        heartbeat=hb_atual
     )
     return {
         "velocidade_sp": percentual_velocidade,
         "angulo_sp": angulo,
         "tipo_movimento_direcional": tipo_movimento,
         "simulacao": simulacao,
-        "heartbeat": heartbeat
+        "heartbeat": hb_atual
     }
 
 def comando_parado():
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/__pycache__/gps_handler.cpython-311.pyc b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/__pycache__/gps_handler.cpython-311.pyc
index c6c956a5b95833f7f677e893cd00fdf1c2ed5e6e..b9c1b965a6a1c949639b02955c5c7550d495d908 100644
GIT binary patch
delta 1010
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delta 325
zcmexm`Nf29IWI340}wp*pOm2~IFWCnfCG>>ogtMWiZO*DiYbMA;(9|?u3!dDo{ev>
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/gps_handler.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/gps_handler.py
index 3ac297c98..14166130a 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/gps_handler.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/shared/gps_handler.py
@@ -111,4 +111,11 @@ class GPSHandler:
         except Exception as e:
             print(f"{time.time()} - [GPSHandler] ❌ Erro ao converter trajetoria para lat e long: {e}")
         return simulacao_latlon
+    
+    def erro_angular(self, angulo_alvo, angulo_atual, angulo_caminho, peso_local=0.5):
+        erro_com_ponto = (angulo_alvo - angulo_atual + np.pi) % (2 * np.pi) - np.pi
+        erro_com_caminho = (angulo_caminho - angulo_atual + np.pi) % (2 * np.pi) - np.pi
+
+        erro_combinado = (1 - peso_local) * erro_com_caminho + peso_local * erro_com_ponto
+        return abs(erro_combinado)
     
\ No newline at end of file
diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/__pycache__/camera_manager.cpython-311.pyc b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/__pycache__/camera_manager.cpython-311.pyc
index 6df54d9148a71c42dac05d07767d73f19b6f2ad0..b0c13326faad2c9c3e9266957a34d4acd27e1187 100644
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diff --git a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/camera_manager.py b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/camera_manager.py
index a97da68d9..ea4f4ce0d 100644
--- a/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/camera_manager.py
+++ b/AgroBase/AgroBase/bin/x64/Debug/Python/Scripts/workers/visual_worker/camera_manager.py
@@ -12,6 +12,7 @@ from visual_worker.processamento.analise_solo import AnaliseSoloManager
 from visual_worker.processamento.analise_anomalias import AnaliseAnomaliasManager
 from visual_worker.processamento.radar_top_down import Radar2DManager
 from visual_worker.processamento.segmentacao_semantica import ClassesSegmentacao, SegmentacaoManager
+from visual_worker.processamento.costmap_fuser import CostmapFuser
 from shared.enums import StatusModulo, T_Code, CameraFrameType
 from shared.utils import analisar_linhas_por_profundidade, decode_image_base64, encode_image_base64, fazer_overlay
 from shared.gps_handler import GPSHandler
@@ -80,6 +81,7 @@ class CameraManager:
             self.solo_manager = AnaliseSoloManager()
             self.segmentacao_manager = SegmentacaoManager(self.camera.colormap_rgb, self.camera.classes)
             self.radar_manager = Radar2DManager()
+            self.data_fuser = CostmapFuser(grid_shape=self.grid_ref_shape, K=3, M=2, fuse_method="q0.7", block_thr=0.7, central_cols=None, y_range_m=(0.5,5.0), near_is_bottom=True, fov_h_rad=np.radians(self.camera.parametros["fov_h"]))
             self.operante = True
             self._timestamp_analise = None
 
@@ -106,14 +108,14 @@ class CameraManager:
         self.iniciando = False
         self.atualizar_saude_camera()
 
-    def _gerar_grid_referencia_geometrico(self, angulo_inclinacao_graus=26, altura_camera_m=0.74):
-        grid_h = self.grid_ref_shape[0]
+    def _gerar_grid_referencia_geometrico(self, angulo_inclinacao_graus=28.91, altura_camera_m=0.74):
+        grid_w, grid_h = self.grid_ref_shape
         def dist_grid_calibrado(grid_h, i, fov, incl, altura):
             alpha_v = ((i + 0.5) / grid_h - 0.5) * np.radians(fov)
             gamma = np.radians(incl) + alpha_v
             d = (altura / np.tan(gamma)) * 1000.0
             return d
-        d = np.array([dist_grid_calibrado(grid_h, i, -43.28, 28.91, 0.74) for i in range(grid_h)], dtype=np.float32)
+        d = np.array([dist_grid_calibrado(grid_h, i, -43.28, angulo_inclinacao_graus, altura_camera_m) for i in range(grid_h)], dtype=np.float32)
         d = d[::-1]  # ordena de baixo->cima como você queria
         return d  # <-- ndarray, não list
 
@@ -240,14 +242,6 @@ class CameraManager:
                         continue
                     elif self.operante and status == StatusModulo.OPERANTE and self.camera is not None:
                         self._realizar_analises()
-                        #if self._ultimo_rgb_frame is not None and self._ultimo_rgb_frame.size > 0 and self._ultima_analise_matriz_custo is not None:
-                        #    self._mostrar_debug_matriz_custo_fundida(self._ultimo_rgb_frame, self._ultima_analise_matriz_custo.get("matriz", [[]]), True)
-                        
-                        #if self._ultima_analise_segmentacao is not None:
-                        #    self.segmentacao_manager.display_segmentation_debug(self._ultimo_rgb_frame, 150)
-                    
-                    #if self._ultimo_rgb_frame is not None and self._ultimo_rgb_frame.size > 0:
-                    #    self._mostrar_grid_distancias_sobre_rgb(self._ultimo_rgb_frame, self.grid_ref)
                 except Exception as e:
                     self.mostrar_log(f"Erro no loop de analise continua: {e}")
                 finally:
@@ -255,12 +249,13 @@ class CameraManager:
                     novo_delay = max(0, (1.0 / freq) - latencia)
                     #self.mostrar_log(
                     #    self._log_performance("Loop", { "freq": _freq, "fps": fps, "latencia": latencia }) + 
-                    #    self._log_performance("Radar", self._ultima_analise_radar) + 
+                    #    #self._log_performance("Radar", self._ultima_analise_radar) + 
                     #    self._log_performance("Segmentacao", self._ultima_analise_segmentacao) + 
                     #    self._log_performance("Matriz Confianca", self._ultima_analise_matriz_confianca) +
-                    #    self._log_performance("Anomalias", self._ultima_analise_anomalias) +
-                    #    self._log_performance("Solo", self._ultima_analise_solo) +
-                    #    self._log_performance("Matriz Custo", self._ultima_analise_matriz_custo) 
+                    #    #self._log_performance("Anomalias", self._ultima_analise_anomalias) +
+                    #    #self._log_performance("Solo", self._ultima_analise_solo) +
+                    #    #self._log_performance("Matriz Custo", self._ultima_analise_matriz_custo) + 
+                    #    ""
                     #)
                     ultima_atualizacao = t0
                     time.sleep(novo_delay)
@@ -307,8 +302,21 @@ class CameraManager:
         cv2.imshow("Grid de Referencia sobre RGB", img)
         cv2.waitKey(1)
 
-
+    
     def _realizar_analises(self):
+        if self._depth_frame_necessario:
+            depth_frame_np, depth_timestamp, depth_res = self.get_depth_frame()
+        else:
+            depth_frame_np = self._ultimo_depth_frame
+        self._analise_segmentacao()
+        parametros_camera = self.camera.parametros
+        fov_h = parametros_camera["fov_h"]
+        distancia_max_m = parametros_camera["distancia_maxima"] / 1000.0
+        self._analise_matriz_confianca(depth_frame_np, distancia_max_m, fov_h)
+        #key, vis = self.debug_show_costmap(rgb_frame=self._ultimo_rgb_frame, grid_dict=self._ultima_analise_matriz_confianca, grid_shape=self.grid_ref_shape, window_name="viz MPC", wait=1, text_mode="mini")
+        key, vis = self.debug_show_visualworker(frame_bgr=self._ultimo_rgb_frame, grid=self._ultima_analise_matriz_confianca, wait=1, text_mode="full", draw_grid=True, draw_cells=True, draw_legend=True)
+
+    def _realizar_analises_async(self):
         executor = self._pool
         tarefas = []
         
@@ -317,9 +325,12 @@ class CameraManager:
         distancia_max_m = parametros_camera["distancia_maxima"] / 1000.0
         percentual_solo = parametros_camera["percentual_altura_solo"] / 100.0
         
-        depth_frame_np, depth_timestamp, depth_res = self.get_depth_frame()
+        if self._depth_frame_necessario:
+            depth_frame_np, depth_timestamp, depth_res = self.get_depth_frame()
+        else:
+            depth_frame_np = self._ultimo_depth_frame
 
-        tarefas.append(executor.submit(self._analise_radar, depth_frame_np, fov_h, distancia_max_m))
+        #tarefas.append(executor.submit(self._analise_radar, depth_frame_np, fov_h, distancia_max_m))
 
         if True or not self._nova_segmentacao_disponivel:
             if not self._analisando_segmentacao:
@@ -330,7 +341,7 @@ class CameraManager:
                 self._nova_segmentacao_disponivel = False
                 tarefas.append(executor.submit(self._analise_matriz_confianca, depth_frame_np, distancia_max_m, fov_h))
 
-        if self._nova_grid_conf_disponivel:
+        if False and self._nova_grid_conf_disponivel:
             limiar_conf: float = 0.4
             matriz_conf = self._ultima_analise_matriz_confianca["matriz"]
             if not self._analisando_anomalias:
@@ -348,21 +359,18 @@ class CameraManager:
 
 
     def _analise_segmentacao(self):
-        if self._analisando_segmentacao:
-            return
+        if self._analisando_segmentacao: return
         self._analisando_segmentacao = True
         try:
             t0 = time.time()
             predictions, ts, res = self.get_segmentation_predictions()
-            if ts == self._ts_segmentacao_anterior:
-                return  # já analisado
+            if ts == self._ts_segmentacao_anterior: return
             self._ts_segmentacao_anterior = ts
             if predictions is not None:
                 rgb_frame, _ts, res = self.get_rgb_frame()
                 analise_segmentacao, log = self.segmentacao_manager.segmentar(predictions)
                 t1 = time.time()
-                if analise_segmentacao == None:
-                    self.mostrar_log(log)
+                if analise_segmentacao == None: self.mostrar_log(log)
                 analise_segmentacao["ultima_chamada"] = self._ultima_analise_segmentacao.get("ultima_chamada", t0)
                 self._calcular_performance(t0, t1, analise_segmentacao)
                 self._ultima_analise_segmentacao = analise_segmentacao
@@ -386,35 +394,31 @@ class CameraManager:
             #self.mostrar_log(f"Segmentacao concluida em {self._ultima_analise_segmentacao['latencia']:.4f} s, a {fps:.4f} FPS")
 
     def _analise_matriz_confianca(self, depth_frame_np, dist_max, fov_h):
-        if self._analisando_matriz_confianca:
-            return
+        if self._analisando_matriz_confianca: return
         self._analisando_matriz_confianca = True
         try:
-            if depth_frame_np is None or depth_frame_np.size == 0:
-                return
+            if depth_frame_np is None or depth_frame_np.size == 0: return
             segmentacao = self._ultima_analise_segmentacao.get("classes")
             if segmentacao is None: return 
-            #segmentacao_vis = self._ultima_analise_segmentacao.get("mask_color", None)
             
-            if depth_frame_np is None or segmentacao is None:
-                self.mostrar_log("❌ Depth frame ou segmentação inválidos para gerar matriz de confiança.")
-            else:
-                t0 = time.time()
-                grid_conf = self._gerar_grid_confianca(depth_frame_np, segmentacao, dist_max)
-                t1 = time.time()
-                grid_conf["ultima_chamada"] = self._ultima_analise_matriz_confianca.get("ultima_chamada", t0)
-                self._calcular_performance(t0, t1, grid_conf)
-                self._ultima_analise_matriz_confianca = grid_conf
-                matriz = self._ultima_analise_matriz_confianca["matriz"]
-                self._ultima_analise_segmentacao["corredor_perfil"] = self.segmentacao_manager.calcular_perfil_corredor(matriz, fov_h)
-                ContextoGlobalRedis.atualizar_ctx_dict(
-                    CtxKey.DadosVisualWorker, 
-                    ts_analise=time.time(),
-                    matrizes__confianca=converter_valores_numpy(matriz),
-                    perfil_corredor__segmentacao=converter_valores_numpy(self._ultima_analise_segmentacao.get("corredor_perfil", []))
-                )
-                self._nova_grid_conf_disponivel = True
-                #self._mostrar_debug_grid_confianca(self._ultimo_rgb_frame, grid_conf["matriz"], True, self._ultima_analise_segmentacao["mask_color"])
+            t0 = time.time()
+            #grid_conf = self._gerar_grid_confianca(depth_frame_np, segmentacao, dist_max)
+            grid_conf = self._construir_grid_confianca(depth_frame_np, segmentacao, self.grid_ref, self.grid_ref_shape, self.camera.classes)
+            #self.mostrar_log(grid_conf)
+            t1 = time.time()
+            grid_conf["ultima_chamada"] = self._ultima_analise_matriz_confianca.get("ultima_chamada", t0)
+            self._calcular_performance(t0, t1, grid_conf)
+            snapshot = self.data_fuser.update(grid_conf, ts=t1)
+            self._ultima_analise_matriz_confianca = grid_conf
+            #self._ultima_analise_segmentacao["corredor_perfil"] = self.segmentacao_manager.calcular_perfil_corredor(matriz, fov_h)
+            ContextoGlobalRedis.atualizar_ctx_dict(
+                CtxKey.DadosVisualWorker, 
+                ts_analise=time.time(),
+                matrizes__confianca=snapshot
+                #perfil_corredor__segmentacao=converter_valores_numpy(self._ultima_analise_segmentacao.get("corredor_perfil", []))
+            )
+            self._nova_grid_conf_disponivel = True
+            #self._mostrar_debug_grid_confianca(self._ultimo_rgb_frame, grid_conf["matriz"], True, self._ultima_analise_segmentacao["mask_color"])
         except Exception as e:
             self.mostrar_log(f"❌ Erro na geracao da matriz de confianca: {e}")
         finally:
@@ -1239,3 +1243,397 @@ class CameraManager:
             cv2.waitKey(1)
         except Exception as e:
             self.mostrar_log(f"❌ Erro ao mostrar debug da matriz de custo: {e}")
+
+
+
+    def _construir_grid_confianca(
+        self, 
+        depth_mm,                # np.ndarray (H,W) em milímetros (0/NaN = inválido)
+        seg_ids_512x288,         # np.ndarray (288,512) com IDs de classe por pixel
+        grid_ref,                # np.ndarray (grid_h,) OU (grid_h, grid_w) em metros
+        grid_shape=(15, 10),     # (cols=15, rows=10)
+        class_ids=None,          # {'rua':X, 'cana':Y, 'obs':Z}
+        valid_mm=(300, 10000),   # faixa válida do depth (ajuste conforme tua OAK)
+        range_m=(0.5, 5.0),      # janela útil à frente (só p/ debug/checar)
+        min_valid_frac=0.30,     # % mínimo de pixels válidos p/ aceitar mediana
+        conf_params=(0.30, 0.80),# t0,t1 p/ mapear %depth_valido -> conf_depth
+        w=(0.6, 0.3, 0.1),       # pesos do custo: classe, anom, (1-conf)
+        anom_tau_min=0.22,       # tolerância mínima de “aproximação” (m)
+        anom_satur_m=0.50        # saturação da anomalia (m)
+    ):
+        """
+        Retorna dict com arrays (grid_h, grid_w):
+        pct_rua, pct_cana, pct_obs, z_med (m), z_ref (m),
+        depth_valid_frac, conf, anom, custo, navegavel (0/1)
+        """
+        if class_ids is None:
+            # AJUSTE para os IDs reais do teu labelmap!
+            class_ids = {'rua': 0, 'cana': 1, 'obs': 2}
+
+        grid_w, grid_h = grid_shape  # (15, 10)
+        H0, W0 = depth_mm.shape
+
+        # --- 1) Reduz o depth para 512x288 preservando rótulos (sem blur de escala) ---
+        d_small = cv2.resize(depth_mm, (512, 288), interpolation=cv2.INTER_NEAREST).astype(np.float32)
+        # marca inválidos
+        d_small[(d_small < valid_mm[0]) | (d_small > valid_mm[1])] = np.nan
+
+        # --- 2) Bordas das células da grid 15x10 sobre a imagem 512x288 ---
+        x_edges = np.linspace(0, 512, grid_w + 1, dtype=int)  # 16 bordas
+        y_edges = np.linspace(0, 288, grid_h + 1, dtype=int)  # 11 bordas
+
+        # --- 3) Saídas (grid_h, grid_w) = (10, 15) ---
+        pct_rua  = np.zeros((grid_h, grid_w), np.float32)
+        pct_cana = np.zeros((grid_h, grid_w), np.float32)
+        pct_obs  = np.zeros((grid_h, grid_w), np.float32)
+        z_med    = np.full((grid_h, grid_w), np.nan, np.float32)
+        depth_valid_frac = np.zeros((grid_h, grid_w), np.float32)
+
+        # --- 4) Garante grid_ref 2D em metros ---
+        if grid_ref.ndim == 1:
+            # grid_ref é por LINHA (grid_h,)
+            if grid_ref.shape[0] != grid_h:
+                raise ValueError(f"grid_ref 1D deve ter len={grid_h}, veio {grid_ref.shape}")
+            Z_ref = np.repeat(grid_ref[:, None], grid_w, axis=1)
+        else:
+            Z_ref = grid_ref
+            if Z_ref.shape != (grid_h, grid_w):
+                raise ValueError(f"grid_ref 2D deve ser {(grid_h, grid_w)}, veio {Z_ref.shape}")
+
+        # --- 5) Agregação por célula (150 células: tranquilo em tempo real) ---
+        for j in range(grid_h):
+            y0, y1 = y_edges[j], y_edges[j+1]
+            seg_row   = seg_ids_512x288[y0:y1, :]  # (rows, 512)
+            depth_row = d_small[y0:y1, :]          # (rows, 512)
+            for i in range(grid_w):
+                x0, x1 = x_edges[i], x_edges[i+1]
+                seg_block   = seg_row[:,  x0:x1]
+                depth_block = depth_row[:, x0:x1]
+
+                n = seg_block.size
+                if n == 0:
+                    continue
+
+                # % por classe
+                n_rua  = np.count_nonzero(seg_block == ClassesSegmentacao.RUA.value)
+                n_cana = np.count_nonzero(seg_block == ClassesSegmentacao.CANA.value)
+                n_obs  = np.count_nonzero(seg_block == ClassesSegmentacao.OBSTACULO.value)
+                pct_rua[j, i]  = n_rua  / n
+                pct_cana[j, i] = n_cana / n
+                pct_obs[j, i]  = n_obs  / n
+
+                # depth: mediana em metros + fração válida
+                vals = depth_block[~np.isnan(depth_block)]
+                valid = vals.size
+                depth_valid_frac[j, i] = valid / n
+                if valid >= max(int(min_valid_frac * n), 1):
+                    z_med[j, i] = np.nanmedian(vals) / 1000.0  # mm -> m
+
+        # --- 6) Confiança por célula ---
+        t0, t1 = conf_params
+        conf_seg = np.maximum.reduce([pct_rua, pct_cana, pct_obs])
+        conf_dep = np.clip((depth_valid_frac - t0) / (t1 - t0), 0.0, 1.0)
+        conf_cell = 0.6 * conf_seg + 0.4 * conf_dep
+
+        # --- 7) Anomalia (obstáculo = mais perto que o esperado) ---
+        # ΔZ > 0 → medido está mais perto que a referência
+        delta = Z_ref - z_med  # m
+        # z_med NaN → delta = 0 (sem evidência)
+        delta = np.where(np.isnan(z_med), 0.0, np.maximum(delta, 0.0))
+        anom_raw = np.clip(delta / anom_satur_m, 0.0, 1.0)
+        # porta de tolerância mínima (tau) + confiança do depth
+        anom = anom_raw * (delta > anom_tau_min).astype(np.float32) * conf_dep
+
+        # --- 8) Custo e navegabilidade ---
+        nao_rua = 1.0 - pct_rua
+        w1, w2, w3 = w
+        custo = np.clip(w1 * nao_rua + w2 * anom + w3 * (1.0 - conf_cell), 0.0, 1.0)
+
+        navegavel = (pct_rua >= 0.55) & (anom < 0.4) & (conf_cell >= 0.5)
+
+        return {
+            "pct_rua": pct_rua,
+            "pct_cana": pct_cana,
+            "pct_obs": pct_obs,
+            "z_med": z_med,                   # metros
+            "z_ref": Z_ref,                   # metros
+            "depth_valid_frac": depth_valid_frac,
+            "conf": conf_cell,                # 0..1
+            "anom": anom,                     # 0..1
+            "custo": custo,                   # 0..1
+            "navegavel": navegavel.astype(np.uint8)  # 0/1
+        }
+    
+    def _put_text_centered(self, img, text, cx, cy, font_scale=0.4, thickness=1, color=(255,255,255), outline=True):
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        (tw, th), baseline = cv2.getTextSize(text, font, font_scale, thickness)
+        x = int(cx - tw/2)
+        y = int(cy + th/2)
+        if outline:
+            cv2.putText(img, text, (x+1, y+1), font, font_scale, (0,0,0), thickness+2, cv2.LINE_AA)
+        cv2.putText(img, text, (x, y), font, font_scale, color, thickness, cv2.LINE_AA)
+
+    def make_costmap_overlay(
+        self, 
+        rgb_frame,                   # HxWx3 (BGR se veio do OpenCV)
+        grid_dict,                   # dict com arrays (grid_h, grid_w): "custo","conf","anom","navegavel","z_med","z_ref","pct_rua","pct_cana","pct_obs"
+        grid_shape=(15,10),          # (cols, rows)
+        alpha=0.45,                  # transparência do heatmap
+        draw_borders=True,
+        draw_cells=True,             # desenha retângulos por célula
+        draw_text_mode="mini",       # "off" | "mini" | "full"
+        draw_legend=True
+    ):
+        #rgb_frame = cv2.resize(rgb_frame, (1080, 720), interpolation=cv2.INTER_AREA)
+        H, W = rgb_frame.shape[:2]
+        grid_w, grid_h = grid_shape
+        custo = grid_dict["custo"].astype(np.float32)            # (grid_h, grid_w) [0..1]
+        conf  = grid_dict.get("conf", None)
+        anom  = grid_dict.get("anom", None)
+        nav   = grid_dict.get("navegavel", None)
+        z_med = grid_dict.get("z_med", None)
+        z_ref = grid_dict.get("z_ref", None)
+        p_rua = grid_dict.get("pct_rua", None)
+        p_can = grid_dict.get("pct_cana", None)
+        p_obs = grid_dict.get("pct_obs", None)
+
+        # 1) heatmap (upscale por vizinho p/ manter blocos nítidos)
+        cost_u8 = np.clip(custo * 255.0, 0, 255).astype(np.uint8)
+        cost_up = cv2.resize(cost_u8, (W, H), interpolation=cv2.INTER_NEAREST)
+        try:
+            cmap = cv2.COLORMAP_TURBO
+        except AttributeError:
+            cmap = cv2.COLORMAP_JET
+        heat = cv2.applyColorMap(cost_up, cmap)
+
+        # 2) overlay
+        out = cv2.addWeighted(heat, alpha, rgb_frame, 1.0 - alpha, 0)
+
+        # 3) grid edges
+        x_edges = (np.linspace(0, W, grid_w+1)).astype(int)
+        y_edges = (np.linspace(0, H, grid_h+1)).astype(int)
+
+        if draw_borders:
+            for x in x_edges:
+                cv2.line(out, (x, 0), (x, H-1), (50,50,50), 1, cv2.LINE_AA)
+            for y in y_edges:
+                cv2.line(out, (0, y), (W-1, y), (50,50,50), 1, cv2.LINE_AA)
+
+        # 4) por-célula (bordas coloridas + textos)
+        if draw_cells:
+            for j in range(grid_h):
+                y0, y1 = y_edges[j], y_edges[j+1]
+                cy = (y0 + y1) // 2
+                for i in range(grid_w):
+                    x0, x1 = x_edges[i], x_edges[i+1]
+                    cx = (x0 + x1) // 2
+
+                    c = float(custo[j, i])
+                    # bordinha: navegável verde, bloqueado vermelho, neutro cinza
+                    if nav is not None:
+                        if nav[j, i]:
+                            color = (60, 200, 60)
+                        else:
+                            color = (20, 20, 220) if c > 0.7 else (90, 90, 90)
+                    else:
+                        color = (90, 90, 90)
+                    cv2.rectangle(out, (x0, y0), (x1-1, y1-1), color, 1)
+
+                    if draw_text_mode != "off":
+                        # texto curtinho (mini) ou completo (full)
+                        if draw_text_mode == "mini":
+                            txt = f"C{int(round(c*100))}"
+                            if conf is not None:
+                                txt += f" cf{int(round(conf[j,i]*100))}"
+                            if anom is not None:
+                                txt += f" A{int(round(anom[j,i]*100))}"
+                            self._put_text_centered(out, txt, cx, cy, font_scale=0.32, thickness=1, color=(255,255,255), outline=True)
+                        elif draw_text_mode == "full":
+                            line1 = f"C{int(round(c*100))}"
+                            if conf is not None: line1 += f" cf{conf[j,i]:.2f}"
+                            if anom is not None: line1 += f" A{int(round(anom[j,i]*100))}"
+                            self._put_text_centered(out, line1, cx, cy-8, font_scale=0.38, thickness=1,
+                                            color=(255,255,255), outline=True)
+                            line2 = ""
+                            if z_med is not None and z_ref is not None and not np.isnan(z_med[j,i]):
+                                line2 = f"Z{z_med[j,i]:.1f}/{z_ref[j,i]:.1f}m"
+                            elif z_ref is not None:
+                                line2 = f"Zref {z_ref[j,i]:.1f}m"
+                            if line2:
+                                self._put_text_centered(out, line2, cx, cy+8, font_scale=0.36, thickness=1,
+                                                color=(255,255,255), outline=True)
+                            if p_rua is not None and p_can is not None and p_obs is not None:
+                                line3 = f"R{int(p_rua[j,i]*100)} C{int(p_can[j,i]*100)} O{int(p_obs[j,i]*100)}"
+                                self._put_text_centered(out, line3, cx, cy+22, font_scale=0.34, thickness=1,
+                                                color=(230,230,230), outline=True)
+
+        # 5) legenda opcional
+        if draw_legend:
+            pad = 8
+            x0, y0 = pad, pad
+            cv2.rectangle(out, (x0-4, y0-4), (x0+160, y0+72), (0,0,0), -1)
+            cv2.putText(out, "Legenda:", (x0, y0+12), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255,255,255), 1, cv2.LINE_AA)
+            cv2.putText(out, "C= custo (0..1)", (x0, y0+28), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (200,200,200), 1, cv2.LINE_AA)
+            cv2.putText(out, "cf= confianca",   (x0, y0+44), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (200,200,200), 1, cv2.LINE_AA)
+            cv2.putText(out, "A= anomalia",     (x0, y0+60), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (200,200,200), 1, cv2.LINE_AA)
+
+        return out
+
+    def debug_show_costmap(self, rgb_frame, grid_dict, grid_shape=(15,10), window_name="debug_costmap", wait=1, text_mode="mini"):
+        vis = self.make_costmap_overlay(rgb_frame, grid_dict, grid_shape=grid_shape, alpha=0.45, draw_borders=True, draw_cells=True, draw_text_mode=text_mode, draw_legend=True)
+        cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+        cv2.imshow(window_name, vis)
+        key = cv2.waitKey(wait) & 0xFF
+        return key, vis
+    
+
+
+    def _normalize_cost_robusto(self, C: np.ndarray):
+        C = np.asarray(C, dtype=np.float32)
+        p5, p95 = np.percentile(C, [5, 95])
+        cmin, cmax = float(p5), float(max(p95, p5 + 1e-6))
+        N = (C - cmin) / (cmax - cmin + 1e-9)
+        return np.clip(N, 0.0, 1.0), cmin, cmax
+
+    def make_visualworker_overlay(
+        self,
+        frame_bgr,                 # frame da câmera (BGR, HxWx3)
+        grid,                      # dict: {"Custo": Hc x Wc, "Navegavel": Hc x Wc, ...}
+        alpha_cost=0.45,           # transparência do heatmap
+        draw_grid=True,
+        draw_cells=True,
+        text_mode="mini",          # "off" | "mini" | "full"
+        draw_legend=True,
+        nav_overlay_alpha=0.35     # opacidade do overlay vermelho p/ NAV=False
+    ):
+        """
+        Sobrepõe Custo (+ Navegável) do Visual Worker no frame RGB, em tempo real.
+        Retorna uma imagem BGR com o overlay.
+        """
+        C = np.asarray(grid.get("custo", None), dtype=np.float32)
+        if C is None or C.size == 0:
+            return frame_bgr.copy()
+
+        NAV = grid.get("navegavel", None)
+        Hc, Wc = C.shape
+        Hf, Wf = frame_bgr.shape[:2]
+
+        # 1) heatmap de custo (robusto a outliers)
+        N, cmin, cmax = self._normalize_cost_robusto(C)       # [0..1]
+        u8 = (N * 255.0).astype(np.uint8)
+        up = cv2.resize(u8, (Wf, Hf), interpolation=cv2.INTER_NEAREST)
+        try:
+            cmap = cv2.COLORMAP_TURBO
+        except AttributeError:
+            cmap = cv2.COLORMAP_JET
+        heat = cv2.applyColorMap(up, cmap)
+        out = cv2.addWeighted(heat, alpha_cost, frame_bgr, 1.0 - alpha_cost, 0)
+
+        # 2) overlay de não-navegável (vermelho)
+        if NAV is not None:
+            bad = (~np.asarray(NAV, dtype=bool)).astype(np.uint8) * 255  # 255 = bloqueado
+            bad_up = cv2.resize(bad, (Wf, Hf), interpolation=cv2.INTER_NEAREST)
+            mask = bad_up.astype(bool)
+            red = out.copy()
+            red[mask] = (0, 0, 255)
+            out = cv2.addWeighted(red, nav_overlay_alpha, out, 1.0 - nav_overlay_alpha, 0)
+
+        # 3) grade
+        x_edges = np.linspace(0, Wf, Wc + 1).astype(int)
+        y_edges = np.linspace(0, Hf, Hc + 1).astype(int)
+        if draw_grid:
+            for x in x_edges:
+                cv2.line(out, (x, 0), (x, Hf - 1), (60, 60, 60), 1, cv2.LINE_AA)
+            for y in y_edges:
+                cv2.line(out, (0, y), (Wf - 1, y), (60, 60, 60), 1, cv2.LINE_AA)
+
+        # 4) textos por célula (mini/full)
+        if draw_cells and text_mode != "off":
+            conf  = grid.get("conf", None)
+            anom  = grid.get("anom", None)
+            z_med = grid.get("z_med", None)
+            z_ref = grid.get("z_ref", None)
+            p_rua = grid.get("pct_rua", None)
+            p_can = grid.get("pct_cana", None)
+            p_obs = grid.get("pct_obs", None)
+
+            for j in range(Hc):
+                y0, y1 = y_edges[j], y_edges[j + 1]
+                cy = (y0 + y1) // 2
+                for i in range(Wc):
+                    x0, x1 = x_edges[i], x_edges[i + 1]
+                    cx = (x0 + x1) // 2
+
+                    # moldura fina, cor conforme NAV
+                    if NAV is not None:
+                        if NAV[j, i]:
+                            color = (60, 200, 60)
+                        else:
+                            color = (20, 20, 220) if C[j, i] > (cmin + 0.7*(cmax-cmin)) else (90, 90, 90)
+                    else:
+                        color = (90, 90, 90)
+                    cv2.rectangle(out, (x0, y0), (x1 - 1, y1 - 1), color, 1, cv2.LINE_AA)
+
+                    # textos
+                    if text_mode == "mini":
+                        # custo em 0..100 + check NAV
+                        c100 = int(round(N[j, i] * 100))
+                        txt = f"C{c100}"
+                        if NAV is not None:
+                            txt += " N" if NAV[j, i] else " B"
+                        self._put_text_centered(out, txt, cx, cy, font_scale=0.32, thickness=1,
+                                                color=(255, 255, 255), outline=True)
+                    elif text_mode == "full":
+                        l1 = f"C{N[j,i]*100:.0f}"
+                        if conf is not None: l1 += f" cf{conf[j,i]:.2f}"
+                        if anom is not None: l1 += f" A{anom[j,i]*100:.0f}"
+                        self._put_text_centered(out, l1, cx, cy - 8, font_scale=0.38, thickness=1,
+                                                color=(255, 255, 255), outline=True)
+                        l2 = ""
+                        if z_med is not None and z_ref is not None and not np.isnan(z_med[j,i]):
+                            l2 = f"Z{z_med[j,i]:.1f}/{z_ref[j,i]:.1f}m"
+                        elif z_ref is not None:
+                            l2 = f"Zref {z_ref[j,i]:.1f}m"
+                        if l2:
+                            self._put_text_centered(out, l2, cx, cy + 8, font_scale=0.36, thickness=1,
+                                                    color=(255, 255, 255), outline=True)
+                        if p_rua is not None and p_can is not None and p_obs is not None:
+                            l3 = f"R{int(p_rua[j,i]*100)} C{int(p_can[j,i]*100)} O{int(p_obs[j,i]*100)}"
+                            self._put_text_centered(out, l3, cx, cy + 22, font_scale=0.34, thickness=1,
+                                                    color=(230, 230, 230), outline=True)
+
+        # 5) DistânciasRef por linha (se disponível)
+        dist_ref = grid.get("DistanciasRef", None)
+        if dist_ref is not None and len(dist_ref) == Hc:
+            for j in range(Hc):
+                y = (y_edges[j] + y_edges[j + 1]) // 2
+                s = f"{float(dist_ref[j]):.2f}m"
+                cv2.putText(out, s, (5, y + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (0, 0, 0), 2, cv2.LINE_AA)
+                cv2.putText(out, s, (5, y + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (255, 255, 255), 1, cv2.LINE_AA)
+
+        # 6) legenda
+        if draw_legend:
+            pad = 8
+            x0, y0 = pad, pad
+            cv2.rectangle(out, (x0 - 4, y0 - 4), (x0 + 190, y0 + 74), (0, 0, 0), -1)
+            cv2.putText(out, "Legenda", (x0, y0 + 14), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)
+            cv2.putText(out, "C: custo (0..1) norm.", (x0, y0 + 32), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (200,200,200), 1, cv2.LINE_AA)
+            cv2.putText(out, "N navegavel / B bloqueado", (x0, y0 + 50), cv2.FONT_HERSHEY_SIMPLEX, 0.42, (200,200,200), 1, cv2.LINE_AA)
+            cv2.putText(out, "turbo=baixo  jet=alto", (x0, y0 + 68), cv2.FONT_HERSHEY_SIMPLEX, 0.38, (160,160,160), 1, cv2.LINE_AA)
+
+        return out
+
+    def debug_show_visualworker(
+        self,
+        frame_bgr,
+        grid,
+        window_name="VW Cost/NAV",
+        wait=1,
+        **overlay_kwargs
+    ):
+        vis = self.make_visualworker_overlay(frame_bgr, grid, **overlay_kwargs)
+        cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+        cv2.imshow(window_name, vis)
+        key = cv2.waitKey(wait) & 0xFF
+        return key, vis
+
diff --git a/Python/OAK/datasets/config.json b/Python/OAK/datasets/config.json
index 387824af5..bcbb8da7b 100644
--- a/Python/OAK/datasets/config.json
+++ b/Python/OAK/datasets/config.json
@@ -1,9 +1,9 @@
 {
-    "camera": "oak-1",
+    "camera": "oak-d",
     "modelo": "fast_scnn",
-    "model_name": "ervas_medium_new",
-    "main_class_name": "erva", 
-    "use_main_class": true,
+    "model_name": "ruas_new",
+    "main_class_name": "cana", 
+    "use_main_class": false,
     "resolucao": [512, 288],
     "roi_inicio": 0.0,
     "roi_tamanho": 1.0