diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml
deleted file mode 100644
index bd74dc04..00000000
--- a/.github/workflows/pages.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-name: Deploy Website
-on:
- push:
- branches: # triggers the workflow on push events to the main branch
- - main
- workflow_dispatch: # allows you to run the workflow manually
-permissions:
- contents: read
- pages: write
- id-token: write
-concurrency:
- group: "pages"
- cancel-in-progress: true
-
-jobs:
- build:
- runs-on: ubuntu-latest
- steps:
- - name: Checkout code
- uses: actions/checkout@v2
- with:
- fetch-depth: 0 # Fetch all history for all branches and tags. This is required for the git revision date plugin and git authors plugin.
- - name: Set up Python
- uses: actions/setup-python@v2
- with:
- python-version: '3.x'
- - name: Install MkDocs and plugins
- run: pip install mkdocs mkdocs-mermaid2-plugin mkdocs-git-revision-date-localized-plugin mkdocs-git-authors-plugin
- - name: Build with MkDocs
- run: mkdocs build
- # - name: Check for hard links and symlinks
- # run: |
- # find site -type l -exec echo "Symlink found: {}" \; -o -type f -links +1 -exec echo "Hard link found: {}" \;
- - name: Upload build artifacts
- uses: actions/upload-pages-artifact@v3
- with:
- name: github-pages
- path: site
-
- deploy:
- environment:
- name: github-pages
- url: ${{ steps.deployment.outputs.page_url }}
- runs-on: ubuntu-latest
- needs: build
- steps:
- - name: Deploy to GitHub Pages
- id: deployment
- uses: actions/deploy-pages@v4
-
diff --git a/.gitignore b/.gitignore
index 212d1a9f..8cf8b99a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,224 @@
-site
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+# Pipfile.lock
+
+# UV
+# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# uv.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+# https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+# in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
+# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+# and can be added to the global gitignore or merged into this file. However, if you prefer,
+# you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml
+
+
+
+# Custom rules
*ignored*
-*__pycache__*
-.vscode/
*.zip
Captured_Frames
Debug
diff --git a/docs/elec/Hat/Hat.kicad_pcb b/Hardware/Hat/Hat.kicad_pcb
similarity index 100%
rename from docs/elec/Hat/Hat.kicad_pcb
rename to Hardware/Hat/Hat.kicad_pcb
diff --git a/docs/elec/Hat/Hat.kicad_prl b/Hardware/Hat/Hat.kicad_prl
similarity index 100%
rename from docs/elec/Hat/Hat.kicad_prl
rename to Hardware/Hat/Hat.kicad_prl
diff --git a/docs/elec/Hat/Hat.kicad_pro b/Hardware/Hat/Hat.kicad_pro
similarity index 100%
rename from docs/elec/Hat/Hat.kicad_pro
rename to Hardware/Hat/Hat.kicad_pro
diff --git a/docs/elec/Hat/Hat.kicad_sch b/Hardware/Hat/Hat.kicad_sch
similarity index 100%
rename from docs/elec/Hat/Hat.kicad_sch
rename to Hardware/Hat/Hat.kicad_sch
diff --git a/docs/elec/Hat/Hat.kicad_sch-bak b/Hardware/Hat/Hat.kicad_sch-bak
similarity index 100%
rename from docs/elec/Hat/Hat.kicad_sch-bak
rename to Hardware/Hat/Hat.kicad_sch-bak
diff --git a/docs/elec/Hat/PCA9517D_112/PCA9517D_112.kicad_sym b/Hardware/Hat/PCA9517D_112/PCA9517D_112.kicad_sym
similarity index 100%
rename from docs/elec/Hat/PCA9517D_112/PCA9517D_112.kicad_sym
rename to Hardware/Hat/PCA9517D_112/PCA9517D_112.kicad_sym
diff --git a/docs/elec/Hat/PCA9517D_112/PCA9517D_112.step b/Hardware/Hat/PCA9517D_112/PCA9517D_112.step
similarity index 100%
rename from docs/elec/Hat/PCA9517D_112/PCA9517D_112.step
rename to Hardware/Hat/PCA9517D_112/PCA9517D_112.step
diff --git a/docs/elec/Hat/PCA9517D_112/SOIC127P600X175-8N.kicad_mod b/Hardware/Hat/PCA9517D_112/SOIC127P600X175-8N.kicad_mod
similarity index 100%
rename from docs/elec/Hat/PCA9517D_112/SOIC127P600X175-8N.kicad_mod
rename to Hardware/Hat/PCA9517D_112/SOIC127P600X175-8N.kicad_mod
diff --git a/docs/elec/Hat/PCA9517D_112/how-to-import.htm b/Hardware/Hat/PCA9517D_112/how-to-import.htm
similarity index 100%
rename from docs/elec/Hat/PCA9517D_112/how-to-import.htm
rename to Hardware/Hat/PCA9517D_112/how-to-import.htm
diff --git a/docs/elec/Hat/fabrication-toolkit-options.json b/Hardware/Hat/fabrication-toolkit-options.json
similarity index 100%
rename from docs/elec/Hat/fabrication-toolkit-options.json
rename to Hardware/Hat/fabrication-toolkit-options.json
diff --git a/docs/elec/Hat/fp-info-cache b/Hardware/Hat/fp-info-cache
similarity index 100%
rename from docs/elec/Hat/fp-info-cache
rename to Hardware/Hat/fp-info-cache
diff --git a/docs/elec/Hat/sym-lib-table b/Hardware/Hat/sym-lib-table
similarity index 100%
rename from docs/elec/Hat/sym-lib-table
rename to Hardware/Hat/sym-lib-table
diff --git a/docs/elec/Hat_CoVASPSy_v1re2_Schema-1.pdf b/Hardware/Hat_CoVASPSy_v1re2_Schema-1.pdf
similarity index 100%
rename from docs/elec/Hat_CoVASPSy_v1re2_Schema-1.pdf
rename to Hardware/Hat_CoVASPSy_v1re2_Schema-1.pdf
diff --git a/docs/elec/Interface/Interface.kicad_pcb b/Hardware/Interface/Interface.kicad_pcb
similarity index 100%
rename from docs/elec/Interface/Interface.kicad_pcb
rename to Hardware/Interface/Interface.kicad_pcb
diff --git a/docs/elec/Interface/Interface.kicad_prl b/Hardware/Interface/Interface.kicad_prl
similarity index 100%
rename from docs/elec/Interface/Interface.kicad_prl
rename to Hardware/Interface/Interface.kicad_prl
diff --git a/docs/elec/Interface/Interface.kicad_pro b/Hardware/Interface/Interface.kicad_pro
similarity index 100%
rename from docs/elec/Interface/Interface.kicad_pro
rename to Hardware/Interface/Interface.kicad_pro
diff --git a/docs/elec/Interface/Interface.kicad_sch b/Hardware/Interface/Interface.kicad_sch
similarity index 100%
rename from docs/elec/Interface/Interface.kicad_sch
rename to Hardware/Interface/Interface.kicad_sch
diff --git a/docs/elec/Interface/fp-info-cache b/Hardware/Interface/fp-info-cache
similarity index 100%
rename from docs/elec/Interface/fp-info-cache
rename to Hardware/Interface/fp-info-cache
diff --git a/docs/elec/Interface_CoVASPSy_v1re2_Schema.pdf b/Hardware/Interface_CoVASPSy_v1re2_Schema.pdf
similarity index 100%
rename from docs/elec/Interface_CoVASPSy_v1re2_Schema.pdf
rename to Hardware/Interface_CoVASPSy_v1re2_Schema.pdf
diff --git a/docs/elec/Mezzanine_CoVASPSy_v1re2_Schema.pdf b/Hardware/Mezzanine_CoVASPSy_v1re2_Schema.pdf
similarity index 100%
rename from docs/elec/Mezzanine_CoVASPSy_v1re2_Schema.pdf
rename to Hardware/Mezzanine_CoVASPSy_v1re2_Schema.pdf
diff --git a/docs/elec/mezzanine/mezzanine.kicad_pcb b/Hardware/mezzanine/mezzanine.kicad_pcb
similarity index 100%
rename from docs/elec/mezzanine/mezzanine.kicad_pcb
rename to Hardware/mezzanine/mezzanine.kicad_pcb
diff --git a/docs/elec/mezzanine/mezzanine.kicad_prl b/Hardware/mezzanine/mezzanine.kicad_prl
similarity index 100%
rename from docs/elec/mezzanine/mezzanine.kicad_prl
rename to Hardware/mezzanine/mezzanine.kicad_prl
diff --git a/docs/elec/mezzanine/mezzanine.kicad_pro b/Hardware/mezzanine/mezzanine.kicad_pro
similarity index 100%
rename from docs/elec/mezzanine/mezzanine.kicad_pro
rename to Hardware/mezzanine/mezzanine.kicad_pro
diff --git a/docs/elec/mezzanine/mezzanine.kicad_sch b/Hardware/mezzanine/mezzanine.kicad_sch
similarity index 100%
rename from docs/elec/mezzanine/mezzanine.kicad_sch
rename to Hardware/mezzanine/mezzanine.kicad_sch
diff --git a/docs/img/Logo/Autotech_original.jpeg b/Logo/Autotech_original.jpeg
similarity index 100%
rename from docs/img/Logo/Autotech_original.jpeg
rename to Logo/Autotech_original.jpeg
diff --git "a/docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole.svg" "b/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole.svg"
similarity index 100%
rename from "docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole.svg"
rename to "Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole.svg"
diff --git "a/docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (long)_promo Ecole.svg" "b/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (long)_promo Ecole.svg"
similarity index 100%
rename from "docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (long)_promo Ecole.svg"
rename to "Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (long)_promo Ecole.svg"
diff --git a/docs/Asservissement.md b/docs/Asservissement.md
deleted file mode 100644
index f9061f2d..00000000
--- a/docs/Asservissement.md
+++ /dev/null
@@ -1 +0,0 @@
-Page asservissement
\ No newline at end of file
diff --git a/docs/Camera.md b/docs/Camera.md
deleted file mode 100644
index 03a1518e..00000000
--- a/docs/Camera.md
+++ /dev/null
@@ -1,4 +0,0 @@
-les mode de camera peuve etre obtenu avec
-
-picam2 = Picamera2()
-picam2.sensor_modes
\ No newline at end of file
diff --git a/docs/Deroulement_course.md b/docs/Deroulement_course.md
deleted file mode 100644
index d13a490c..00000000
--- a/docs/Deroulement_course.md
+++ /dev/null
@@ -1,98 +0,0 @@
-# Déroulement de la course
-
-Cette page sert à détailler les différentes étapes auxquelles vous pouvez/devez participer au cours de l'année.
-
-## Tests sur pistes
-
-Respectivement un et deux mois avant la course, ainsi que les 2 jours qui la précèdent, l'ENS vous ouvre ses portes pour des essais sur piste. Ils auront installé une piste en utilisant les mêmes composants que le jour de la course sur laquelle vous pouvez essayer de faire tourner votre voiture.
-
-!!! warning
-Ces essais sont très importants ! Faites au maximum pour y aller ! En effet, votre voiture peut très bien tourner sur le simulateur ou sur une piste artisanale si vous en avez une mais se comporter de manière très différente dans les conditions de la course. C'est une occasion de ne pas travailler dans le vide et de vous assurer concrètement de la validité de l'avancement de votre projet.
-
-### Où aller
-
-Ces essais ont lieu au même endroit qu'aura lieu la course. Rendez-vous donc à l'ENS (4 Avenue des Sciences, 91190 Gif-sur-Yvette).
-
-- Passez les 2 portes tournantes, vous arrivez dans la cour interne.
-- Suivez l'allée jusqu'au bout.
-- Passez la porte et prenez l'escalier à gauche.
-- Montez 2 étages.
-- Dans le corps principal du batiment (longue allée avec plusieurs étages de plateforme), allez tout au fond à droite.
-
-### A ne pas oublier
-
-Pensez à embarquer tout votre matériel. Voiture, batteries, testeur/chargeur de batterie, câbles de remplacement, mini-routeur diffusant du Intech_Second, vos PCs, etc. C'est un peu loin pour faire l'aller-retour si vous avez oublié des choses.
-
-## Homologation
-
-Il s'agit de la première partie de la course. Vous devez pouvoir montrer que votre voiture respecte les critères définis par le règlement.
-
-!!! Note
-Vous pouvez faire votre homologation la veille vers la fin de journée si vous êtes encore là pour vos essais sur pistes (ce qui est fortement recommandé !!). Si vous le pouvez, faites le, cela sera un énorme gain de temps (et diminution de stress) pour vous le jour J.
-
-L'homologation se déroule de la façon suivante :
-
--
-
-## Le contre-la-montre
-
-Vous voici le jour J, homologué, prêts à en découdre. Félicitations pour cette première étape !
-Vous passez donc maintenant à la première phase des qualifications : le contre-la-montre !
-
-### Aparte : pour la com
-
-Avant de vous parler des courses, petit conseil pour la com : c'est le moment de faire des rushs (je rappelle que vous devez faire un clip à la fin du GATE). Si vous voulez avoir une bonne vue des courses, mettez vous sur les plateformes en hauteur pour filmer (un étage suffit). Et hésitez pas à filmer d'autres moments de la journée, vous savez pas de quoi vous aurez besoin pour le clip (et puis ça fait des moments sympas entre vous 😉).
-
-### Revenons à nos moutons
-
-Le principe est simple : vous devez effectuer 2 tours de piste seul et avec obstacles le plus rapidement possible. Vous avez pour cela 2 essais (séparés par un peu de temps pour vous permettre de corriger des choses si le premier se déroule mal). Ne sera retenu évidemment que le meilleur des 2 chronos. Si jamais vous ne parvenez pas à terminer les 2 tours, votre temps sera mis au maximum (le maximum sera normalement défini un peu au-dessus du temps de la voiture ayant fini les 2 tours avec le plus haut temps).
-
-Un classement sera alors dressé qui pemerttra de définir les poules pour la seconde phase des qualifications.
-
-C'est maintenant l'heure de la pause du midi, bon appétit !
-
-## Courses à plusieurs
-
-Passons à la deuxième phase des qualifications. Vous allez affronter d'autres voitures sur pistes cette fois (4 à 8 voitures selon le nombre de voitures présentes à la course), et ce, sur 3 tours.
-
-A chaque course la voiture accumule des points pour le classement général des qualifications : 10 points pour le premier, 6 points pour le 2nd, 4 pour le 3ème, et 2 points pour les voitures ayant terminé la course. Une voiture ne bouclant pas les 3 tours n'obtient aucun point.
-
-!!! note
-
-Pour information, pour notre année, le fait de ne pas terminer les 3 tours faisait que l'on avait 0 point, même si on les avait quasiment fait (par exemple, se crasher au dernier virage, on parle d'expérience 😭). M. Taillandier-Loize a indiqué qu'il demanderait à ce que cette règle soit rediscutée pour plutôt offrir des points par tour. On espère que ça sera accepté.
-
-!!! warning
-
-Attention, voici des règles importantes :
-
-- Les équipes ont 3 min pour installer leur véhicule sur la piste.
-- L’ensemble des véhicules de la poule est positionné sur la grille de départ selon les résultats des qualifications.
-- Une fois que toutes les équipes ont annoncé être prêtes, il est interdit de toucher les véhicules. Le signal de départ est donné oralement par l’arbitre.
-- L’ordre d’arrivée est relevé après un nombre de tours définis à l’avance (3 par défaut pour la phase de qualification et 5 pour les phases finales).
-- Une voiture ne terminant pas le nombre de tours définis n'est pas classée.
-- Un véhicule ayant un comportement notoirement agressif envers les véhicules adverses est disqualifié retiré de la piste, de même qu'une voiture empêchant volontairement une autre de la doubler.
-- Un véhicule immobilisé sur la piste plus de 10 secondes en l'absence d'une voiture le bloquant est retiré de la piste.
-- Un véhicule ayant parcouru plus de 2 m à contre-sens est retiré de la piste.
-
-## Finales
-
-On espère que tout s'est bien passé pour vous jusqu'ici ! C'est le moment, la dernière ligne droite, l'heure de briller !
-
-Bon pour cette partie je vais reprendre la docu de la course :
-
-A l'issue des qualifications, chaque voiture obtient 2 scores :
-
-- le total coursesQualif (la somme des points obtenus lors des 2 courses de qualification)
-- le total CLM+courseQualif(le score précédent plus les points obtenus en qualification contre la montre).
-
-2 manches sont composées de chacune de 3 courses.
-
-Les 2 premiers de chaque poule (avec le score courseQualif, le score CLM+courseQualif servant à départager les ex-aequo) font les courses 1 de chaque manche, les 3ème et 4ème font les courses 2 de chaque manche et les autres font la course 3 de chaque manche. Le classement des qualifications (avec le score courseQualif, le score CLM+courseQualif servant à départager les ex-aequo) détermine la grille de départ, la voiture ayant le plus de points s’élançant en tête sur la grille de départ.
-
-Les arbitres relèvent l’ordre d’arrivée après un nombre de tours définis à l’avance (5 par défaut) et attribuent les points de la manière suivante : 25 pts pour le 1er, 18 pts pour le 2e, 15 pts pour le 3e, 12 pts pour le 4e ; 10 pts pour le 5e, 8 pts pour le 6e ; 6 pts pour le 7e, 4 pts pour le 8e ; 2 pts pour le 9e et 1 pt pour le 10e. Une voiture ne terminant pas le nombre de tours défini obtient 0 point. Une fois les voitures de la première course classées, on continue l'attribution des points aux voitures de la 2nde course.
-
-Le classement final se fait avec les points des 2 courses finales uniquement. En cas d'égalité, le score CLM+courseQualif est pris en compte pour le classement final. Si il y a de nouveau égalité, le score coursesQualif départage les équipes.
-
-Les prix récompensent les 1er, 2ème, 3ème du classement général. S'y ajoute un prix de l'innovation et un prix pour les 1ers parmi les licences.
-
-Et voilà, CoVAPSy, c'est finiii... On espère que vous avez tout cassé (sauf la voiture svp) ! La partie technique du GATE est globalement finie, il vous reste donc les actions de promotions à faire, bon courage 😉
diff --git a/docs/IA.md b/docs/IA.md
deleted file mode 100644
index dc07f627..00000000
--- a/docs/IA.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-Raceline is a Trakmania IA its journey is desribed here: [https://www.youtube.com/watch?v=cUojVsCJ51I](https://www.youtube.com/watch?v=cUojVsCJ51I)
-[https://linesight-rl.github.io/linesight/build/html/index.html](https://linesight-rl.github.io/linesight/build/html/index.html)
-[https://arxiv.org/abs/1806.06923](https://arxiv.org/abs/1806.06923)
-
diff --git a/docs/Instruction_manual_UST-10LX_MRS0020D_en_1513910662-1.pdf b/docs/Instruction_manual_UST-10LX_MRS0020D_en_1513910662-1.pdf
deleted file mode 100644
index 511f6217..00000000
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diff --git a/docs/Lidar.md b/docs/Lidar.md
deleted file mode 100644
index 2ee9fbc9..00000000
--- a/docs/Lidar.md
+++ /dev/null
@@ -1,106 +0,0 @@
-# Lidar
-
-## UST-10lx basics
-
-
-```mermaid
-flowchart TD
- A("`__fa:fa-car TT02_0__
- Hokuyo
- (_AI model_)`")
-```
-
-
----
-## Organisation complexe à _n_ voitures
-
-Pour s'affranchir de ces limites on propose de déplacer l'éxécution du model dans un controller séparé des _n_ voitures. On choisira donc de crééer un [Robot](https://cyberbotics.com/doc/reference/robot) de type [Supervisor](https://cyberbotics.com/doc/reference/supervisor) qu'on nommera `WorldSupervisor` en ajoutant le field `supervisor TRUE` dans la string de définition du node correspondant à notre supervisor. Notre objectif est de créer _n_ voitures à l'initialisation du monde et de faire communiquer les voitures et le WorldSupervisor via des [Emitters](https://www.cyberbotics.com/doc/reference/emitter)/[Receivers](https://www.cyberbotics.com/doc/reference/receiver). Il faut donc que le WorldSupervisor ait _n_ couples d'[Emitters](https://www.cyberbotics.com/doc/reference/emitter)/[Receivers](https://www.cyberbotics.com/doc/reference/receiver) pour communiquer à chaque voiture sur un channel différent.
-
-!!! warning
- _n_ n'est pas connu avant l'éxecution du controller du WorldSupervisor. Or, l'état d'un [Robot](https://cyberbotics.com/doc/reference/robot) pour les fonctions de controller dans Webots ne change pas dynamiquement i.e. si le WorldSupervisor s'ajoute lui-même des devices via son controller au cours de l'éxecution, ces derniers ne seront pas acessibles via la méthode `getDevice(name)`.
-
-C'est pour cela qu'en plus d'un WorldSupervisor gérant la logique de l'[IA](./IA.md), nous avons besoin d'un autre [Supervisor](https://cyberbotics.com/doc/reference/supervisor) dont le rôle est de créer le WorldSupervisor avec ses _n_ couples d'[Emitters](https://www.cyberbotics.com/doc/reference/emitter)/[Receivers](https://www.cyberbotics.com/doc/reference/receiver) et les _n_ voitures au début de l'initialisation du monde. On nommera ce nouveau [Supervisor](https://cyberbotics.com/doc/reference/supervisor) `WorldInit`.
-
-!!! warning
- L'API [Emitters](https://www.cyberbotics.com/doc/reference/emitter)/[Receivers](https://www.cyberbotics.com/doc/reference/receiver) ne garantit pas un planning spécifique pour la transmission. Il arrive parfois que plusieurs paquets soient regroupés et reçus ensemble ou qu'aucun packet ne soit reçu. Ces cas doivent donc être traités et on perd le déterminisme de l'entraînement.
-
----
-### Pendant l'initialisation du monde
-
-
-```mermaid
-flowchart TD
- Init("WorldInit") --> |crée| A("`__WorldSupervisor__
- (_AI model_)
- supervisor_emitter_0
- supervisor_receiver_0
- supervisor_emitter_1
- supervisor_receiver_1
- ...
- supervisor_emitter_n-1
- supervisor_receiver_n-1`")
-
- Init --> |crée| B("`__fa:fa-car TT02_0__
-
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-
- Init --> |crée| C("`__fa:fa-car TT02_1__
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-
- Init --> D[...]
- style D fill:none,stroke:none
- linkStyle 3 stroke-width:0,marker-end:none
- Init --> |crée| E("`__fa:fa-car TT02_n-1__
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-```
-
-
----
-### Pendant l'exécution des controllers
-
-```mermaid
-flowchart TD
- Super("`__WorldSupervisor__
- supervisor_emitter_0
- supervisor_receiver_0
- supervisor_emitter_1
- supervisor_receiver_1
- ...
- supervisor_emitter_n-1
- supervisor_receiver_n-1`") --> |commande| B("`__fa:fa-car TT02_0__
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-
- Super --> |commande| C("`__fa:fa-car TT02_1__
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-
- Super --> D[...]
- style D fill:none,stroke:none
- linkStyle 2 stroke-width:0,marker-end:none
-
- Super --> |commande| E("`__fa:fa-car TT02_n-1__
- Hokuyo
- TT02_emitter
- TT02_receiver`")
-```
-
-
----
-## Environnement Webots utilisé
-
-### Piste
-
-Le monde utilisé est `Simulateur/worlds/piste2.wbt`. Ce monde vient de la version 2023 du [github officiel CoVAPsy](https://github.com/ajuton-ens/CourseVoituresAutonomesSaclay).
-
-### Voiture
-
-La voiture utilisé est TT02(`Simulateur/protos/TT02_2023b.proto`). Ce proto vient de la version 2023 du [github officiel CoVAPsy](https://github.com/ajuton-ens/CourseVoituresAutonomesSaclay).
-
-### Capteurs
-
-Les capteurs utilisés sont :
-
-- Un [Lidar](./Lidar.md) aux specifications de notre vrai lidar.
-
-- Un [TouchSensor](https://cyberbotics.com/doc/reference/touchsensor) uniquement sur le simulateur pour détecter les collisions.
diff --git a/docs/UV_USAGE.md b/docs/UV_USAGE.md
deleted file mode 100644
index b7cc2bdb..00000000
--- a/docs/UV_USAGE.md
+++ /dev/null
@@ -1,127 +0,0 @@
-# UV Dependency Management Guide
-
-This project now uses [UV](https://github.com/astral-sh/uv) for fast, reliable Python package management.
-
-## Quick Start
-
-### Install UV
-```bash
-curl -LsSf https://astral.sh/uv/install.sh | sh
-source $HOME/.cargo/env
-```
-
-### Install Project Dependencies
-```bash
-# Install all runtime dependencies
-uv sync
-
-# Install with documentation dependencies
-uv sync --extra docs
-
-# Install with development dependencies
-uv sync --extra dev
-
-# Install all optional dependencies
-uv sync --all-extras
-```
-
-### Running Python Scripts and Commands
-```bash
-# Run a script using the project's virtual environment
-uv run python src/HL/main.py
-
-# Run a script directly (if defined in pyproject.toml)
-uv run covapsy-main
-
-# Run any command with the project's dependencies
-uv run mkdocs serve
-
-# Note: Always use 'uv run' prefix to ensure you're using the UV-managed environment
-```
-
-### Managing Dependencies
-
-#### Add a new dependency
-```bash
-# Add runtime dependency
-uv add numpy
-
-# Add development dependency
-uv add --dev pytest
-
-# Add documentation dependency
-uv add --optional docs mkdocs
-```
-
-#### Remove a dependency
-```bash
-uv remove package-name
-```
-
-#### Update dependencies
-```bash
-# Update all packages
-uv lock --upgrade
-
-# Update specific package
-uv add package-name@latest
-```
-
-### Virtual Environment Management
-
-UV automatically manages virtual environments. You can also:
-
-```bash
-# Activate the virtual environment manually
-source .venv/bin/activate
-
-# Or run commands directly
-uv run python your_script.py
-```
-
-### Lock File
-
-The `uv.lock` file pins exact versions for reproducible builds. Commit this file to version control.
-
-
-### Old way (pip):
-```bash
-pip install -r requirements.txt
-```
-
-### New way (UV):
-```bash
-uv sync
-```
-
-## Benefits of UV
-
-- **Fast**: 10-100x faster than pip
-- **Reliable**: Deterministic resolution with lock files
-- **Cross-platform**: Works on Linux, macOS, Windows
-- **Drop-in replacement**: Compatible with existing Python tooling
-- **Modern**: Built in Rust with modern dependency resolution
-
-## Troubleshooting
-
-### Common Issues
-
-#### "Plugin not installed" or "Module not found" errors
-**Problem**: Running commands directly (e.g., `mkdocs serve`) instead of through UV.
-**Solution**: Always prefix commands with `uv run`:
-```bash
-# ❌ Wrong
-mkdocs serve
-
-# ✅ Correct
-uv run mkdocs serve
-```
-
-#### UV installs unwanted dependencies
-**Problem**: UV may incorrectly resolve optional dependencies due to dependency resolution bugs.
-**Solution**: Remove problematic lock files and clear cache:
-```bash
-rm uv.lock
-uv cache clean
-uv sync --extra docs
-```
diff --git a/docs/building_docs.md b/docs/building_docs.md
deleted file mode 100644
index 51352797..00000000
--- a/docs/building_docs.md
+++ /dev/null
@@ -1,175 +0,0 @@
-# How to use Markdown and MkDocs
-
-This documentation is built using Markdown and MkDocs. This page is a guide to get you started contributing.
-
-## What is Markdown
-
-> Markdown is a lightweight markup language used to format text in a simple and readable way. It uses plain-text syntax to define elements like headings, lists, links, and emphasis (e.g., **bold**, *italic*). Markdown is commonly used in documentation, blogging platforms, and version control systems like GitHub because it is easy to write and converts seamlessly into HTML for web display. -ChatGPT
-
-Markdown files are often recognizable by their `.md` and rarely `.markdown` file extensions.
-
-## Basic Syntax
-
-### Bold and Italics
-
-To make bold text, surround your text with double asterisks:
-
-
- **This text will be bolded**
- This text will be bolded
-
-
-For italics, use a single asterisk:
-
-
- *This text will be italicized*
- This text will be italicized
-
-
-These can be combined:
-
-
- ***This text will be bold and italic***
- This text will be bold and italic
-
-
-### Headings
-
-Headings are denoted using `#` at the beginning of a line. The number of `#` determines the depth of the header:
-
-| Input | Render |
-|------------------|------------------ |
-| # Heading 1 | Heading 1 |
-| ## Heading 2 | Heading 2 |
-| ### Heading 3 | Heading 3 |
-| #### Heading 4 | Heading 4 |
-| ##### Heading 5 | Heading 5 |
-
-!!! warning
- A space is required for a heading to work.
-
- - \#Heading 1 ❌
- - \## Heading 1 ✔️
-
-### Links
-
-To add links, use the following syntax:
-
-\[The text that will be displayed](The link to follow)
-
-\[A link to example.org](https://example.org/)
-
-[A link to example.org](https://example.org/)
-
-!!! Note
- These can be used for internal links as well. For example, we can link this section with \[Links](#Links) or \[Links](/building_docs/#Links): [Links](#links).
-
- The second method can be generalized to reach any page or header on the website.
-
-### Images
-
-The syntax to add images is almost the same as links. Alt text is the text used by accessibility readers. It should therefore be descriptive rather than a title.
-
-\!\[Alt text](path/to/image.png)
-
-\!\[A square version of the autotech logo. It is used as a favicon for the site](img/favicon.png)
-
-
-
-!!! Note
- See [Images in HTML](#images-in-html) for more fine-grain control of images.
-
-### Code Blocks
-
-Code blocks in Markdown can be created using triple backticks (\`\`\`). You can specify the language for syntax highlighting by adding the language name after the opening backticks. Here is an example of a code block in Python:
-
-\`\`\`python
-
-def hello_world():
-
- print("Hello, world!")
-
-\`\`\`
-
-```python
-def hello_world():
- print("Hello, world!")
-```
-
-You can also create inline code by wrapping text in single backticks (\`). For example, \`print("Hello, world!")\` renders as `print("Hello, world!")`.
-
-## Advanced Syntax
-
-Markdown's strength is in the simplicity of writing, but that sometimes comes back to bite us when trying to do something a little more complicated. Luckily, most Markdown renderers accept some HTML in Markdown files. This is the case with MkDocs and GitHub. **HTML should only be used as necessary.**
-
-### Comments
-
-In Markdown, there is no built-in syntax for comments. However, you can use HTML comments to add notes or comments that will not be rendered in the final output. Here is an example:
-
-```
-
-```
-
-### Images in HTML
-
-Images are impossible to scale or render next to text. Using the HTML `
-
-### Admonitions
-
-Admonitions are a way to highlight important information in your documentation. MkDocs supports several types of admonitions, such as notes, warnings/caution, danger, and tips/hint. Here is an example of how to use them:
-
-```
-!!! note
- This is a note admonition.
-```
-!!! note
- This is a note admonition.
-
-```
-!!! warning
- This is a warning admonition.
-```
-!!! warning
- This is a warning admonition.
-
-## Using MkDocs
-
-For full documentation, visit [mkdocs.org](https://www.mkdocs.org).
-
-### Commands
-
-* `mkdocs serve` - Start the live-reloading docs server. It should now be accessible at [127.0.0.1:8000](http://127.0.0.1:8000/) on ***your machine**** `mkdocs gh-deploy` - Build the documentation site on the gh-pages branch of your repository.
-
-!!! Note
- On the [Club-INTech/CoVAPSy](https://github.com/Club-INTech/CoVAPSy) repository, `mkdocs gh-deploy` is automatically called upon push to the main branch. It can still be manually called when on another branch, but [gh-pages](https://github.com/Club-INTech/CoVAPSy/tree/gh-pages) will be overwritten by any push on main.
-
-### Project Layout
-
- mkdocs.yml ## The configuration file.
- docs/
- index.md ## The documentation homepage.
- ... ## Other markdown pages, images, and other files.
- img/
- favicon.png ## The icon that appears on the browser tab.
- ... other images
-
-### Using GitHub Pages
-
-Navigate to the repository, then to settings/pages. Using the drop-down menus, select "Deploy from branch" for source and "gh-pages" and "/root" for branch.
-
-!!! Note
- You may have to run `mkdocs gh-deploy` for the gh-pages branch to show. See [Commands](#commands).
diff --git "a/docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole_transparent.png" "b/docs/img/Logo/Logo_\303\251quipe N\302\26020_AutoTech 2025 (carr\303\251)_promo Ecole_transparent.png"
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diff --git a/docs/img/UST-10lx.jpg b/docs/img/UST-10lx.jpg
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diff --git a/docs/img/favicon.ico b/docs/img/favicon.ico
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diff --git a/docs/index.md b/docs/index.md
deleted file mode 100755
index 134fbf30..00000000
--- a/docs/index.md
+++ /dev/null
@@ -1,11 +0,0 @@
-# Home
-
-Bonjour à toi, cher INTech-ien intéressé par AutoTech. Ces pages seront ta bible pour commencer à contribuer à AutoTech.
-
-Cette documentation a pour objectif d’être autosuffisante, mais comme aucune documentation n’est parfaite, n’hésite pas à te rapprocher de tes 2A.
-
-Le conseil le plus précieux que je puisse te donner est de documenter au fur et à mesure, et d’utiliser exclusivement Git.
-
-Utilise Git, car tu seras amené à faire des modifications par-ci par-là, et notamment pendant le rush de la course, tu pourrais être tenté de faire du scp (ssh copy). Résiste. Faire ça pose problème, car tu risques de perdre des versions de code qui fonctionnaient bien. Dans la même veine, utilise et abuse de la fonctionnalité Release de GitHub. Elle te sera très utile lorsque tu seras dans le rush et que plus rien ne fonctionnera.
-
-Enfin, documente au fur et à mesure. Tu pourrais être tenté de dire : « C’est le rush, je documenterai plus tard. » Résiste là aussi. Premièrement, tu pourrais mieux comprendre certains éléments en les documentant proprement. Deuxièmement, tu risques d’oublier des choses. Et troisièmement, tu pourrais te retrouver à rédiger la documentation le 17 septembre, alors que ta première réunion avec les 1A est le 18 septembre et que tu n’as pas touché au projet depuis la mi-avril.
\ No newline at end of file
diff --git a/docs/requirments.txt b/docs/requirments.txt
deleted file mode 100644
index 99f97a46..00000000
--- a/docs/requirments.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-mkdocs
-mkdocs-mermaid2-plugin
-mkdocs-git-revision-date-localized-plugin
-mkdocs-git-authors-plugin
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 048b860f..86d17b9e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,9 @@
[project]
-name = "Autotech"
+name = "autotech"
version = "0.1.0"
-description = "Participation de INTech à CoVAPSy"
+description = "INTech participation to CoVAPSy"
readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12,<3.14"
authors = [
{ name = "Arnaud Costermans", email = "arnaud.costermans@intech-robotics.fr" },
{ name = "Matthias Bienvenu", email = "matthias.bienvenu@intech-robotics.fr" },
@@ -13,55 +13,47 @@ authors = [
{ name = "Cyril Bampeta", email = "cyril.bampeta@intech-robotics.fr" },
]
-dependencies = [
-]
+# dependencies = [
+# ]
-[project.optional-dependencies]
-docs = [
- "mkdocs>=1.4.0",
- "mkdocs-mermaid2-plugin>=0.6.0",
- "mkdocs-git-revision-date-localized-plugin>=1.1.0",
- "mkdocs-git-authors-plugin>=0.6.0",
-]
-simu = [
- # Core scientific computing and data processing
- "numpy>=1.21.0",
- "matplotlib>=3.4.0",
+# [project.optional-dependencies]
+# docs = [
+# "mkdocs>=1.4.0",
+# "mkdocs-mermaid2-plugin>=0.6.0",
+# "mkdocs-git-revision-date-localized-plugin>=1.1.0",
+# "mkdocs-git-authors-plugin>=0.6.0",
+# ]
+# rpi = [
+# # Note: picamera2 causes UV to auto-include rpi extras even when not requested
+# # This is likely a UV bug. Add back when needed:
+# # "picamera2>=0.3.0",
+# # AI Inference
+# "onnxruntime>=1.8.0",
+# # Raspberry Pi specific hardware control
+# "rpi-hardware-pwm>=0.1.0",
+# "RPi.GPIO>=0.7.1",
+# "gpiozero>=1.6.0",
+# # I2C and SPI communication (Linux specific)
+# "smbus>=1.1.post2",
+# "spidev>=3.5",
+# # OLED display support
+# "luma.oled>=3.8.0",
+# "luma.core>=2.3.0",
+# "Adafruit-SSD1306>=1.6.0",
+# # Time-of-Flight sensor support
+# "adafruit-circuitpython-vl53l0x>=3.6.0",
+# "adafruit-blinka>=8.0.0",
+# # PS4 controller support
+# "pyPS4Controller>=1.2.0",
+# "zmq>=0.0.0",
+# "netifaces>=0.11.0",
+# "matplotlib>=3.10.5",
+# "opencv-python>=4.12.0.88",
+# "scipy>=1.15.3",
+# ]
- # Machine learning and AI
- "onnx>=1.8.0",
- "onnxruntime>=1.8.0",
- "torch>=1.9.0",
- "stable-baselines3>=1.6.0",
-
- # Used in the controller
- "psutil[simu]>=7.1.3",
-]
-rpi = [
- # Note: picamera2 causes UV to auto-include rpi extras even when not requested
- # This is likely a UV bug. Add back when needed:
- # "picamera2>=0.3.0",
- # AI Inference
- "onnxruntime>=1.8.0",
- # Raspberry Pi specific hardware control
- "rpi-hardware-pwm>=0.1.0",
- "RPi.GPIO>=0.7.1",
- "gpiozero>=1.6.0",
- # I2C and SPI communication (Linux specific)
- "smbus>=1.1.post2",
- "spidev>=3.5",
- # OLED display support
- "luma.oled>=3.8.0",
- "luma.core>=2.3.0",
- "Adafruit-SSD1306>=1.6.0",
- # Time-of-Flight sensor support
- "adafruit-circuitpython-vl53l0x>=3.6.0",
- "adafruit-blinka>=8.0.0",
- # PS4 controller support
- "pyPS4Controller>=1.2.0",
- "zmq>=0.0.0",
- "netifaces>=0.11.0",
- "matplotlib>=3.10.5",
- "opencv-python>=4.12.0.88",
- "scipy>=1.15.3",
+[tool.uv.workspace]
+members = [
+ "src/high_level",
+ "src/simulation",
]
diff --git a/scripts/launch_train_multiprocessing.py b/scripts/launch_train_multiprocessing.py
deleted file mode 100644
index edc13a85..00000000
--- a/scripts/launch_train_multiprocessing.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import logging
-import os
-import sys
-
-from typing import *
-
-import torch.nn as nn
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-simu_path = __file__.rsplit('/', 2)[0] + '/src/Simulateur'
-if simu_path not in sys.path:
- sys.path.insert(0, simu_path)
-
-from Simulateur.config import LOG_LEVEL
-from config import *
-from TemporalResNetExtractor import TemporalResNetExtractor
-from CNN1DResNetExtractor import CNN1DResNetExtractor
-from onnx_utils import *
-
-from WebotsSimulationGymEnvironment import WebotsSimulationGymEnvironment
-if LOG_LEVEL == logging.DEBUG: from DynamicActionPlotCallback import DynamicActionPlotDistributionCallback
-
-
-if __name__ == "__main__":
-
- if not os.path.exists("/tmp/autotech/"):
- os.mkdir("/tmp/autotech/")
-
- os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
-
-
- def make_env(simulation_rank: int, vehicle_rank: int):
- if LOG_LEVEL == logging.DEBUG:
- print("CAREFUL !!! created an SERVER env with {simulation_rank}_{vehicle_rank}")
- return WebotsSimulationGymEnvironment(simulation_rank, vehicle_rank)
-
- envs = SubprocVecEnv([lambda simulation_rank=simulation_rank, vehicle_rank=vehicle_rank : make_env(simulation_rank, vehicle_rank) for vehicle_rank in range(n_vehicles) for simulation_rank in range(n_simulations)])
-
- ExtractorClass = CNN1DResNetExtractor
-
- policy_kwargs = dict(
- features_extractor_class=ExtractorClass,
- features_extractor_kwargs=dict(
- context_size=context_size,
- lidar_horizontal_resolution=lidar_horizontal_resolution,
- camera_horizontal_resolution=camera_horizontal_resolution,
- device=device
- ),
- activation_fn=nn.ReLU,
- net_arch=[512, 512, 512],
- )
-
-
- ppo_args = dict(
- n_steps=4096,
- n_epochs=10,
- batch_size=256,
- learning_rate=3e-4,
- gamma=0.99,
- verbose=1,
- normalize_advantage=True,
- device=device
- )
-
-
- save_path = __file__.rsplit("/", 1)[0] + "/checkpoints/" + ExtractorClass.__name__ + "/"
- os.makedirs(save_path, exist_ok=True)
-
-
- print(save_path)
- print(os.listdir(save_path))
-
- valid_files = [x for x in os.listdir(save_path) if x.rstrip(".zip").isnumeric()]
-
- if valid_files:
- model_name = max(
- valid_files,
- key=lambda x : int(x.rstrip(".zip"))
- )
- print(f"Loading model {save_path + model_name}")
- model = PPO.load(
- save_path + model_name,
- envs,
- **ppo_args,
- policy_kwargs=policy_kwargs
- )
- i = int(model_name.rstrip(".zip")) + 1
- print(f"----- Model found, loading {model_name} -----")
-
- else:
- model = PPO(
- "MlpPolicy",
- envs,
- **ppo_args,
- policy_kwargs=policy_kwargs
- )
-
- i = 0
- print("----- Model not found, creating a new one -----")
-
- print("MODEL HAS HYPER PARAMETERS:")
- print(f"{model.learning_rate=}")
- print(f"{model.gamma=}")
- print(f"{model.verbose=}")
- print(f"{model.n_steps=}")
- print(f"{model.n_epochs=}")
- print(f"{model.batch_size=}")
- print(f"{model.device=}")
-
- print("SERVER : finished executing")
-
- # obs = envs.reset()
- # while True:
- # action, _states = model.predict(obs, deterministic=True) # Use deterministic=True for evaluation
- # obs, reward, done, info = envs.step(action)
- # envs.render() # Optional: visualize the environment
-
-
- while True:
- export_onnx(model)
- test_onnx(model)
-
- if LOG_LEVEL <= logging.DEBUG:
- model.learn(total_timesteps=500_000, callback=DynamicActionPlotDistributionCallback())
- else:
- model.learn(total_timesteps=500_000)
-
- print("iteration over")
-
- model.save(save_path + str(i))
-
- i += 1
diff --git a/src/HL/README.md b/src/HL/README.md
deleted file mode 100644
index 42c2e728..00000000
--- a/src/HL/README.md
+++ /dev/null
@@ -1 +0,0 @@
-[HokuyoReader.py](HokuyoReader.py) now Lidar.py is included with permission from the author [cassc](https://gist.github.com/cassc/26ac479624cb028b2567491a68c34fb8) who graciously released his code under MIT.
\ No newline at end of file
diff --git a/src/Simulateur/.idea/.gitignore b/src/Simulateur/.idea/.gitignore
deleted file mode 100644
index 13566b81..00000000
--- a/src/Simulateur/.idea/.gitignore
+++ /dev/null
@@ -1,8 +0,0 @@
-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml
diff --git a/src/Simulateur/.idea/Simulateur.iml b/src/Simulateur/.idea/Simulateur.iml
deleted file mode 100644
index 88391539..00000000
--- a/src/Simulateur/.idea/Simulateur.iml
+++ /dev/null
@@ -1,14 +0,0 @@
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/src/Simulateur/.idea/inspectionProfiles/profiles_settings.xml b/src/Simulateur/.idea/inspectionProfiles/profiles_settings.xml
deleted file mode 100644
index 105ce2da..00000000
--- a/src/Simulateur/.idea/inspectionProfiles/profiles_settings.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-
-
\ No newline at end of file
diff --git a/src/Simulateur/.idea/misc.xml b/src/Simulateur/.idea/misc.xml
deleted file mode 100644
index d9c6cb89..00000000
--- a/src/Simulateur/.idea/misc.xml
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-
-
-
\ No newline at end of file
diff --git a/src/Simulateur/.idea/modules.xml b/src/Simulateur/.idea/modules.xml
deleted file mode 100644
index 276197d7..00000000
--- a/src/Simulateur/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/src/Simulateur/.idea/vcs.xml b/src/Simulateur/.idea/vcs.xml
deleted file mode 100644
index 6c0b8635..00000000
--- a/src/Simulateur/.idea/vcs.xml
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-
-
-
\ No newline at end of file
diff --git a/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym.py b/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym.py
deleted file mode 100644
index 4ed5f7fa..00000000
--- a/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym.py
+++ /dev/null
@@ -1,188 +0,0 @@
-"""Voiture autonome avec utilisation d'un
-LIDAR sur WEBOTS
-Auteur: Chrysanthe et Jessica
-"""
-
-import numpy as np
-import random
-import gymnasium as gym
-import time
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_checker import check_env
-from stable_baselines3.common.vec_env import SubprocVecEnv
-
-from vehicle import Driver
-from controller import Lidar
-from controller import Field
-from controller import Supervisor
-
-
-#--------------GYM----------------------------
-
-#Création de l'environnement GYM
-class WebotsGymEnvironment(gym.Env):
- def __init__(self):
- #Initialisation du driver
- self.driver = Driver()
-
- basicTimeStep = int(self.driver.getBasicTimeStep())
- self.sensorTime = basicTimeStep//4
-
- #Paramètre de la voiture (position, etc..)
- self.voiture_robot = self.driver.getFromDef("vehicle")
-
- #Lidar
- self.lidar = self.driver.getDevice('lidar')
- self.lidar.enable(self.sensorTime)
- self.lidar.enablePointCloud()
-
- #Capteur de distance
- self.capteur_avant = self.driver.getDevice('front_center_sensor')
- self.capteur_gauche = self.driver.getDevice('side_left_sensor')
- self.capteur_droite = self.driver.getDevice('side_right_sensor')
- self.capteur_avant.enable(self.sensorTime)
- self.capteur_gauche.enable(self.sensorTime)
- self.capteur_droite.enable(self.sensorTime)
-
- #Capteur de balise
- self.capteur_balise = self.driver.getDevice('capteur_balise')
- self.capteur_balise.enable(self.sensorTime)
-
- self.action_space = gym.spaces.Discrete(5) #actions disponibles
- min = np.zeros(self.lidar.getNumberOfPoints())
- max = np.ones(self.lidar.getNumberOfPoints())
- self.observation_space = gym.spaces.Box(min, max, dtype=np.float32) #Etat venant du LIDAR
-
- self.trans_champs = self.voiture_robot.getField("translation")
- self.rot_champs = self.voiture_robot.getField("rotation") # idk why but if this goes befor Lidar it will not work
-
-
- #Vérification de l'état de la voiture
- def observe(self):
- try:
- tableau = self.lidar.getRangeImage()
- #Division par 10 pour que la valeur soient entre 0 et 1
- etat = np.array(tableau, dtype=np.float32)/10
- except: #En cas de non retour lidar
- print("Pas de retour du lidar")
- etat = np.zeros(self.lidar.getNumberOfPoints(), dtype=np.float32)
-
- return etat
-
- #Remise à 0 pour l'environnement GYM
- def reset(self, seed=0):
- #self.capteur_avant.disable()
- #self.capteur_gauche.disable()
- #self.capteur_droite.disable()
-
- #Valeur aléatoire
- x = random.uniform(1.5, 1.65)
- y = random.uniform(3.66, 3.8)
-
- #Fonction d'initialisation
- INITIAL_trans = [x, y, 0.0195182]
- INITIAL_rot=[-0.000257, 0.000618, 1 , -0.784]
- self.trans_champs.setSFVec3f(INITIAL_trans)
- self.rot_champs.setSFRotation(INITIAL_rot)
-
- time.sleep(0.3) #Temps de pause après réinitilialisation
-
- #self.capteur_avant.enable(self.sensorTime)
- #self.capteur_gauche.enable(self.sensorTime)
- #self.capteur_droite.enable(self.sensorTime)
- #Retour état
- obs = self.observe()
- #super().step()
- info = {}
- return obs, info
-
- #Fonction step de l'environnement GYM
- def step(self, action):
- self.driver.setSteeringAngle([-.4, -.1, 0, .1, .4][action])
- self.driver.setCruisingSpeed(1.0)
-
- obs = self.observe()
-
- reward = 0
- done = False
- truncated = False
-
- avant = self.capteur_avant.getValue()
- gauche = self.capteur_gauche.getValue()
- droite = self.capteur_droite.getValue()
- balise = self.capteur_balise.getValue()
-
- if avant >= 900 and not(done):
- print("Collision avant")
- reward = -100
- done = True
- elif ((avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937) and not(done):
- print("Collision gauche")
- reward = -100
- done = True
- elif ((avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961) and not(done):
- print("Collision droite")
- reward = -100
- done = True
- elif balise > 700:
- done = False
- print("Balise passée")
- reward = 20
- else:
- done = False
- reward = 0
-
- self.driver.step()
-
- return obs, reward, done, truncated, {}
-
- #Fonction render de l'environnement GYM
- def render(self, mode="human", close=False):
- pass
-
-
-#----------------Programme principal--------------------
-def main():
- n_vehicles = 4
- # env = SubprocVecEnv([lambda: WebotsGymEnvironment() for i in range(n_vehicles)])
- env = WebotsGymEnvironment()
- check_env(env)
-
- logdir = "./Webots_tb/"
- #-- , tensorboard_log = logdir -- , tb_log_name = "PPO_voiture_webots"
-
- #Définition modèle avec paramètre par défaut
- model = PPO('MlpPolicy', env,
- n_steps=2048,
- n_epochs=10,
- batch_size=32,
- learning_rate=3e-3,
- verbose=1,
- device=device
- )
-
- #Entrainnement
- model.learn(total_timesteps=1e6)
-
- #Sauvegarde
- model.save("Voiture_autonome_Webots_PPO")
-
- #del model
-
- #Chargement des données d'apprentissage
- #model = PPO.load("Voiture_autonome_Webots_PPO")
-
- obs = env.reset()
-
- for _ in range(1000000):
- #Prédiction pour séléctionner une action à partir de l'observation
- action, _states = model.predict(obs, deterministic=True)
- obs, reward, done, info = env.step(action)
- if done:
- obs = env.reset()
-
-
-if __name__ == '__main__':
- main()
-
diff --git a/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym_v2.py b/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym_v2.py
deleted file mode 100644
index c05ecff6..00000000
--- a/src/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym/controller_Gym_v2.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""Voiture autonome avec utilisation d'un
-LIDAR sur WEBOTS
-Auteur : Chrysanthe et Jessica
-"""
-
-import numpy as np
-import random
-import gymnasium as gym
-import time
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_checker import check_env
-
-from vehicle import Driver
-from controller import Lidar
-from controller import Field
-from controller import Supervisor
-
-
-#--------------GYM------- ---------------------
-
-#Création de l'environnement GYM
-class WebotsGymEnvironment(Driver, gym.Env) :
- def __init__(self):
- super().__init__() #Objet héritant la classe Driver
-
- basicTimeStep = int(super().getBasicTimeStep())
- self.sensorTime = basicTimeStep//4
-
- #Lidar
- self.lidar = Lidar("lidar")
-
- #Capteur LIDAR
- self.lidar.enable(self.sensorTime)
- self.lidar.enablePointCloud()
-
- self.action_space = gym.spaces.Discrete(5) #actions disponibles
-
- min = np.zeros(self.lidar.getNumberOfPoints())
- max = np.ones(self.lidar.getNumberOfPoints())
- self.observation_space = gym.spaces.Box(min, max, dtype=np.float32) #Etat venant du LIDAR
-
-
- #Paramètre de la voiture (position, etc..)
- self.voiture_robot = super().getFromDef("vehicle")
- self.trans_champs = self.voiture_robot.getField("translation")
- self.rot_champs = self.voiture_robot.getField("rotation")
-
-
- #Capteur de distance
- self.capteur_avant = super().getDevice('front_center_sensor')
- self.capteur_gauche = super().getDevice('side_left_sensor')
- self.capteur_droite = super().getDevice('side_right_sensor')
- self.capteur_avant.enable(self.sensorTime)
- self.capteur_gauche.enable(self.sensorTime)
- self.capteur_droite.enable(self.sensorTime)
-
- #Capteur de balise
- self.capteur_balise = super().getDevice('capteur_balise')
- self.capteur_balise.enable(self.sensorTime)
-
- #Vérification de l'état de la voiture
- def observe(self) :
- try :
- tableau = self.lidar.getRangeImage()
- #Division par 10 pour que la valeur soient entre 0 et 1
- etat = np.divide(np.array(tableau),10)
- print("etat : " + etat)
- except : #En cas de non retour lidar
- print("Pas de retour du lidar")
- etat = np.zeros(self.lidar.getNumberOfPoints())
-
- return np.array(etat).astype('float32')
-
- #Remise à 0
- def initialisation(self) :
- #self.capteur_avant.disable()
- #self.capteur_gauche.disable()
- #self.capteur_droite.disable()
-
- #Valeur aléatoire
- x = random.uniform(1.5, 1.65)
- y = random.uniform(3.66, 3.8)
-
- #Fonction d'initialisation
- INITIAL_trans = [x, y, 0.0195182]
- INITIAL_rot=[-0.000257, 0.000618, 1 , -0.784]
- self.trans_champs.setSFVec3f(INITIAL_trans)
- self.rot_champs.setSFRotation(INITIAL_rot)
-
- time.sleep(0.3) #Temps de pause après réinitilialisation
-
- #self.capteur_avant.enable(self.sensorTime)
- #self.capteur_gauche.enable(self.sensorTime)
- #self.capteur_droite.enable(self.sensorTime)
-
- #Remise à 0 pour l'environnement GYM
- def reset(self):
- self.initialisation()
- #Retour état
- obs = self.observe()
- #super().step()
- return obs
-
- #Fonction pour detection de collision et attribution des récompenses
- def evaluer(self):
- recompense = 0
- done = False
-
- avant = self.capteur_avant.getValue()
- gauche = self.capteur_gauche.getValue()
- droite = self.capteur_droite.getValue()
- balise = self.capteur_balise.getValue()
-
- if avant >= 900 and not(done) :
- print("Collision avant")
- recompense = -100
- done = True
- elif ((avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937) and not(done) :
- print("Collision gauche")
- recompense = -100
- done = True
- elif ((avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961) and not(done) :
- print("Collision droite")
- recompense = -100
- done = True
- elif balise > 700 :
- done = False
- print("Balise passée")
- recompense = 200
- else :
- done = False
- recompense = 0
-
- return recompense, done
-
- #Fonction pour déplacer la voiture
- def rouler(self, action) :
- super().setCruisingSpeed(1.0)
-
- if action == 0 :
- super().setSteeringAngle(-0.4)
-
- elif action == 1 :
- super().setSteeringAngle(-0.1)
-
- elif action == 2 :
- super().setSteeringAngle(0)
-
- elif action == 3 :
- super().setSteeringAngle(0.1)
-
- elif action == 4 :
- super().setSteeringAngle(0.4)
-
- #Fonction step de l'environnement GYM
- def step(self, action):
- self.rouler(action)
-
- obs = self.observe()
-
- reward, done = self.evaluer()
-
- super().step()
-
- return obs, reward, done, {}
-
- #Fonction render de l'environnement GYM
- def render(self, mode="human", close=False):
- pass
-
-
-#----------------Programme principal--------------------
-def main() :
- env = WebotsGymEnvironment()
- check_env(env)
-
- logdir = "./Webots_tb/"
- #-- , tensorboard_log = logdir -- , tb_log_name = "PPO_voiture_webots"
-
- #Définition modèle avec paramètre par défaut
- model = PPO('MlpPolicy', env,
- n_steps = 256,
- ent_coef = 0.01,
- n_epochs = 5,
- batch_size= 32,
- learning_rate= 3e-3,
- verbose=1)
-
- #Entrainnement
- model.learn(total_timesteps=1e6)
-
- #Sauvegarde
- model.save("Voiture_autonome_Webots_PPO")
-
- #del model
-
- #Chargement des données d'apprentissage
- #model = PPO.load("Voiture_autonome_Webots_PPO")
-
- obs = env.reset()
-
- for _ in range(1000000):
- #Prédiction pour séléctionner une action à partir de l'observation
- action, _states = model.predict(obs, deterministic=True)
- obs, reward, done, info = env.step(action)
- if done:
- obs = env.reset()
-
-
-if __name__ == '__main__' :
- main()
-
diff --git a/src/Simulateur/DynamicActionPlotCallback.py b/src/Simulateur/DynamicActionPlotCallback.py
deleted file mode 100644
index 359b32b6..00000000
--- a/src/Simulateur/DynamicActionPlotCallback.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-from stable_baselines3.common.callbacks import BaseCallback
-import torch
-import torch.nn.functional as F
-from config import *
-
-
-steering_true = (n_actions_steering + 1) // 2
-
-class DynamicActionPlotDistributionCallback(BaseCallback):
- def __init__(self, verbose=0):
- super().__init__(verbose)
- self.fig, self.ax = plt.subplots(4, 1, figsize=(10, 8))
-
- # Steering bars
- self.steering_bars = self.ax[0].bar(range(n_actions_steering), np.zeros(n_actions_steering), color='blue')
- self.steering_avg = [
- self.ax[0].plot([0, 0], [0, 1], color=(i/3, 1 - i/3, 0), label='Average')[0]
- for i in range(4)
- ]
- self.ax[0].set_ylim(0, 1) # Probabilities range from 0 to 1
- self.ax[0].set_title('Steering Action Probabilities')
-
- # Speed bars
- self.speed_bars = self.ax[1].bar(range(n_actions_speed), np.zeros(n_actions_speed), color='blue')
- self.speed_avg = self.ax[1].plot([0, 0], [0, 1], color='red', label='Average')[0]
- self.ax[1].set_ylim(0, 1) # Probabilities range from 0 to 1
- self.ax[1].set_title('Speed Action Probabilities')
-
- # LiDAR img
- self.lidar_img = self.ax[2].imshow(
- np.zeros((lidar_horizontal_resolution, lidar_horizontal_resolution)),
- cmap='gray', vmin=0, vmax=np.log(31)
- )
- self.ax[2].set_title('LiDAR Image')
-
- # Camera img
- self.camera_img = self.ax[3].imshow(
- np.zeros((camera_horizontal_resolution, camera_horizontal_resolution, 3)),
- cmap='RdYlGn', vmin=-1, vmax=1
- )
- self.ax[3].set_title('Camera Image')
-
- def _on_step(self) -> bool:
- global steering_true
- # Get the action probabilities
-
- obs = self.locals["obs_tensor"].clone().detach()
-
- self.lidar_img.set_array(np.log(1 + obs[0, 0, :, :].cpu().numpy()))
- self.camera_img.set_array(obs[0, 1, :, :].cpu().numpy())
- with torch.no_grad():
- latent = self.model.policy.features_extractor(obs)
- extracted = self.model.policy.mlp_extractor.policy_net(latent)
- output = self.model.policy.action_net(extracted)[0]
-
- T = [0.4, 0.6, 0.8, 1.0]
- steer_probs = torch.softmax(output[:n_actions_steering], dim=-1).to("cpu")
- speed_probs = torch.softmax(output[n_actions_steering:], dim=-1).to("cpu")
-
-
-
- # Update the probabilities
- for i, bar in enumerate(self.steering_bars):
- bar.set_height(steer_probs[i].item())
-
- for i in range(4):
- steer_probs_T = torch.softmax(output[:n_actions_steering] / T[i], dim=-1).to("cpu")
- steering_avg = (steer_probs_T / steer_probs_T.sum() * torch.arange(16)).sum().item()
- self.steering_avg[i].set_xdata([steering_avg, steering_avg])
-
-
-
- for i, bar in enumerate(self.speed_bars):
- bar.set_height(speed_probs[i].item())
-
- speed_avg = (speed_probs * torch.arange(n_actions_speed)).sum().item()
- self.speed_avg.set_xdata([speed_avg, speed_avg])
-
- plt.draw()
- plt.pause(1e-8)
-
- return True # Continue training
diff --git a/src/Simulateur/WebotsSimulationGymEnvironment.py b/src/Simulateur/WebotsSimulationGymEnvironment.py
deleted file mode 100644
index 213b0688..00000000
--- a/src/Simulateur/WebotsSimulationGymEnvironment.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import os
-from typing import *
-import numpy as np
-import gymnasium as gym
-
-from config import *
-
-
-class WebotsSimulationGymEnvironment(gym.Env):
- """
- One environment for each vehicle
-
- n: index of the vehicle
- supervisor: the supervisor of the simulation
- """
-
- def __init__(self, simulation_rank: int, vehicle_rank: int):
-
-
- super().__init__()
- self.simulation_rank = simulation_rank
- self.vehicle_rank = vehicle_rank
-
- self.handler = logging.FileHandler(f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.vehicle_rank}.log")
- self.handler.setFormatter(FORMATTER)
- self.log = logging.getLogger("SERVER")
- self.log.setLevel(level=LOG_LEVEL)
- self.log.addHandler(self.handler)
-
-
- self.log.info("Initialisation started")
-
- # this is only true if lidar_horizontal_resolution = camera_horizontal_resolution
- box_min = np.zeros([2, context_size, lidar_horizontal_resolution], dtype=np.float32)
- box_max = np.ones([2, context_size, lidar_horizontal_resolution], dtype=np.float32) * 30
-
- self.observation_space = gym.spaces.Box(box_min, box_max, dtype=np.float32)
- self.action_space = gym.spaces.MultiDiscrete([n_actions_steering, n_actions_speed])
-
- if not os.path.exists("/tmp/autotech"):
- os.mkdir("/tmp/autotech")
-
- self.log.debug(f"Creation of the pipes")
-
- os.mkfifo(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe")
- os.mkfifo(f"/tmp/autotech/serverto{simulation_rank}_{vehicle_rank}.pipe")
- os.mkfifo(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}tosupervisor.pipe")
-
- # --mode=fast --minimize --no-rendering --batch --stdout
- if vehicle_rank == 0 :
- os.system(f"""
- webots {__file__.rsplit('/', 1)[0]}/worlds/piste{simulation_rank % n_map}.wbt --mode=fast --minimize --batch --stdout &
- echo $! {simulation_rank}_{vehicle_rank} >>/tmp/autotech/simulationranks
- """)
-
- self.log.debug("Connection to the vehicle")
- self.fifo_w = open(f"/tmp/autotech/serverto{simulation_rank}_{vehicle_rank}.pipe", "wb")
- self.log.debug("Connection to the supervisor")
- self.fifo_r = open(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe", "rb")
-
- self.log.info("Initialisation finished\n")
-
- def reset(self, seed=0):
- # basically useless function
-
- # lidar data
- # this is true for lidar_horizontal_resolution = camera_horizontal_resolution
- self.context = obs = np.zeros([2, context_size, lidar_horizontal_resolution], dtype=np.float32)
- info = {}
- self.log.info(f"reset finished\n")
- return obs, info
-
- def step(self, action):
-
- self.log.info("Starting step")
- self.log.info(f"sending {action=}")
- self.fifo_w.write(action.tobytes())
- self.fifo_w.flush()
-
- # communication with the supervisor
- self.log.debug("trying to get info from supervisor")
- cur_state = np.frombuffer(self.fifo_r.read(np.dtype(np.float32).itemsize * (n_sensors + lidar_horizontal_resolution + camera_horizontal_resolution)), dtype=np.float32)
- self.log.info(f"received {cur_state=}")
- reward = np.frombuffer(self.fifo_r.read(np.dtype(np.float32).itemsize), dtype=np.float32)[0] # scalar
- self.log.info(f"received {reward=}")
- done = np.frombuffer(self.fifo_r.read(np.dtype(np.bool).itemsize), dtype=np.bool)[0] # scalar
- self.log.info(f"received {done=}")
- truncated = np.frombuffer(self.fifo_r.read(np.dtype(np.bool).itemsize), dtype=np.bool)[0] # scalar
- self.log.info(f"received {truncated=}")
- info = {}
-
- cur_state = np.nan_to_num(cur_state[n_sensors:], nan=0., posinf=30.)
-
- lidar_obs = cur_state[:lidar_horizontal_resolution]
- camera_obs = cur_state[lidar_horizontal_resolution:]
-
- self.context = obs = np.concatenate([
- self.context[:, 1:],
- [lidar_obs[None], camera_obs[None]]
- ], axis=1)
-
- self.log.info("step over")
-
- return obs, reward, done, truncated, info
-
-
diff --git a/src/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py b/src/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py
deleted file mode 100644
index d65b69d7..00000000
--- a/src/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import numpy as np
-import psutil
-import time
-import os
-import re
-import sys
-import logging
-
-# add src/Simulateur to sys.path
-path = __file__.rsplit('/', 3)[0]
-sys.path.insert(0, path)
-
-from config import *
-from vehicle import Driver
-
-class VehicleDriver(Driver):
- """
- This class is a subclass of the Driver class and is used to control the vehicle.
- It basically receives instructions from the controllerWorldSupervisor and follows them.
- """
-
- def __init__(self):
- super().__init__()
-
-
- basicTimeStep = int(self.getBasicTimeStep())
- self.sensorTime = basicTimeStep // 4
-
- self.v_min = 1
- self.v_max = 9
-
- self.i = int(self.getName().split("_")[-1])
- # Lidar
- self.lidar = self.getDevice("Hokuyo")
- self.lidar.enable(self.sensorTime)
- self.lidar.enablePointCloud()
-
- # Camera
- self.camera = self.getDevice("RASPI_Camera_V2")
- self.camera.enable(self.sensorTime)
-
- # Checkpoint sensor
- self.touch_sensor = self.getDevice("touch_sensor")
- self.touch_sensor.enable(self.sensorTime)
-
- # Communication
-
- proc = psutil.Process(os.getpid()) #current
- parent = proc.parent() #parent
- grandparent = parent.parent() if parent else None #grandparent
- pppid = str(grandparent.pid)
-
-
- self.simulation_rank = int(
- re.search(
- pppid + r" (\d+)",
- open("/tmp/autotech/simulationranks", "r").read(),
- re.MULTILINE
- ).group(1)
- )
-
- self.handler = logging.FileHandler(f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.i}.log")
- self.handler.setFormatter(FORMATTER)
- self.log = logging.getLogger("CLIENT")
- self.log.setLevel(level=LOG_LEVEL)
- self.log.addHandler(self.handler)
-
- self.log.debug("Connection to the server")
- self.fifo_r = open(f"/tmp/autotech/serverto{self.simulation_rank}_{self.i}.pipe", "rb")
- self.log.debug("Connection with the supervisor")
- self.fifo_w = open(f"/tmp/autotech/{self.simulation_rank}_{self.i}tosupervisor.pipe", "wb")
-
-
-
-
- #Vérification de l"état de la voiture
- def observe(self):
- try:
- sensor_data = [np.array(self.touch_sensor.getValue(), dtype=np.float32)]
-
- lidar_data = np.array(self.lidar.getRangeImage(), dtype=np.float32)
-
- camera_data = np.array(self.camera.getImageArray(), dtype=np.float32)
- # shape = (1080, 1, 3)
- camera_data = camera_data.transpose(1, 2, 0)[0]
- # shape = (3, 1080)
- color = np.argmax(camera_data, axis=0)
- camera_data = (
- (color == 0).astype(np.float32)*-1 +
- (color == 1).astype(np.float32)*1 +
- (color == 2).astype(np.float32)*0
- )
- # red -> -1
- # green -> 1
- # blue -> 0
-
- return np.concatenate([
- sensor_data,
- lidar_data,
- camera_data
- ])
- except:
- #En cas de non retour lidar
- return np.concatenate([
- np.array(self.touch_sensor.getValue(), dtype=np.float32),
- np.zeros(self.lidar.getNumberOfPoints(), dtype=np.float32)
- ])
-
- #Fonction step de l"environnement GYM
- def step(self):
- # sends observation to the supervisor
-
- # First to be executed
-
- self.log.info("Starting step")
- obs = self.observe()
- self.log.info(f"Observe {obs=}")
- self.fifo_w.write(obs.tobytes())
- self.fifo_w.flush()
-
- self.log.debug("Trying to read action from the server")
- action = np.frombuffer(self.fifo_r.read(np.dtype(np.int64).itemsize * 2), dtype=np.int64)
- self.log.info(f"received {action=}")
-
- # Simulation step
-
- action_steering = np.linspace(-.4, .4, n_actions_steering, dtype=np.float32)[action[0], None]
- action_speed = np.linspace(self.v_min, self.v_max, n_actions_speed, dtype=np.float32)[action[1], None]
-
- cur_angle = self.getSteeringAngle()
- dt = self.getBasicTimeStep()
- omega = 20 # rad/s (max angular speed of the steering servo)
-
- action_steering = cur_angle + np.clip(action_steering - cur_angle, -omega * dt, omega * dt)
-
- self.setSteeringAngle(action_steering)
- self.setCruisingSpeed(action_speed)
-
- return super().step()
-
- def run(self):
- # this call is just there to make sure at least one step
- # is done in the entire simulation before we call lidar.getRangeImage()
- # otherwise it will crash the controller with the message:
- # WARNING: 'controllerVehicleDriver' controller crashed.
- # WARNING: controllerVehicleDriver: The process crashed some time after starting successfully.
- super().step()
- while self.step() != -1:
- pass
-
-
-#----------------Programme principal--------------------
-def main():
- driver = VehicleDriver()
- driver.log.info("Starting the vehicle driver\n")
- driver.run()
-
-
-if __name__ == "__main__":
- print("Starting the vehicle driver")
- main()
diff --git a/src/high_level/.python-version b/src/high_level/.python-version
new file mode 100644
index 00000000..24ee5b1b
--- /dev/null
+++ b/src/high_level/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/src/Simulateur/__init__.py b/src/high_level/README.md
similarity index 100%
rename from src/Simulateur/__init__.py
rename to src/high_level/README.md
diff --git a/src/high_level/pyproject.toml b/src/high_level/pyproject.toml
new file mode 100644
index 00000000..361785bd
--- /dev/null
+++ b/src/high_level/pyproject.toml
@@ -0,0 +1,14 @@
+[project]
+name = "high-level"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+authors = [
+ { name = "matthiasbienvenu", email = "matthias.bienvenu@telecom-sudparis.eu" }
+]
+requires-python = ">=3.12, <3.14"
+dependencies = []
+
+[build-system]
+requires = ["uv_build>=0.9.24,<0.10.0"]
+build-backend = "uv_build"
diff --git a/src/high_level/src/high_level/__init__.py b/src/high_level/src/high_level/__init__.py
new file mode 100644
index 00000000..2551051c
--- /dev/null
+++ b/src/high_level/src/high_level/__init__.py
@@ -0,0 +1,2 @@
+def hello() -> str:
+ return "Hello from high-level!"
diff --git a/src/high_level/src/high_level/py.typed b/src/high_level/src/high_level/py.typed
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/Urg_information_win-1.0.2/COPYING.txt b/src/low_level/scripts/Urg_information_win-1.0.2/COPYING.txt
similarity index 100%
rename from scripts/Urg_information_win-1.0.2/COPYING.txt
rename to src/low_level/scripts/Urg_information_win-1.0.2/COPYING.txt
diff --git a/scripts/Urg_information_win-1.0.2/README.txt b/src/low_level/scripts/Urg_information_win-1.0.2/README.txt
similarity index 100%
rename from scripts/Urg_information_win-1.0.2/README.txt
rename to src/low_level/scripts/Urg_information_win-1.0.2/README.txt
diff --git a/scripts/Urg_viewer_win-2.0.7/COPYING.txt b/src/low_level/scripts/Urg_viewer_win-2.0.7/COPYING.txt
similarity index 100%
rename from scripts/Urg_viewer_win-2.0.7/COPYING.txt
rename to src/low_level/scripts/Urg_viewer_win-2.0.7/COPYING.txt
diff --git a/scripts/Urg_viewer_win-2.0.7/README.txt b/src/low_level/scripts/Urg_viewer_win-2.0.7/README.txt
similarity index 100%
rename from scripts/Urg_viewer_win-2.0.7/README.txt
rename to src/low_level/scripts/Urg_viewer_win-2.0.7/README.txt
diff --git a/scripts/Urg_viewer_win-2.0.7/Urg_viewer_ja.qm b/src/low_level/scripts/Urg_viewer_win-2.0.7/Urg_viewer_ja.qm
similarity index 100%
rename from scripts/Urg_viewer_win-2.0.7/Urg_viewer_ja.qm
rename to src/low_level/scripts/Urg_viewer_win-2.0.7/Urg_viewer_ja.qm
diff --git a/scripts/Urg_viewer_win-2.0.7/address.txt b/src/low_level/scripts/Urg_viewer_win-2.0.7/address.txt
similarity index 100%
rename from scripts/Urg_viewer_win-2.0.7/address.txt
rename to src/low_level/scripts/Urg_viewer_win-2.0.7/address.txt
diff --git a/scripts/Urg_viewer_win-2.0.7/glew32.dll b/src/low_level/scripts/Urg_viewer_win-2.0.7/glew32.dll
similarity index 100%
rename from scripts/Urg_viewer_win-2.0.7/glew32.dll
rename to src/low_level/scripts/Urg_viewer_win-2.0.7/glew32.dll
diff --git a/src/LL/Asserv2/Asserv2.ino b/src/low_level/src/Asserv2/Asserv2.ino
similarity index 100%
rename from src/LL/Asserv2/Asserv2.ino
rename to src/low_level/src/Asserv2/Asserv2.ino
diff --git a/src/LL/Asserv2/I2Cslave.cpp b/src/low_level/src/Asserv2/I2Cslave.cpp
similarity index 100%
rename from src/LL/Asserv2/I2Cslave.cpp
rename to src/low_level/src/Asserv2/I2Cslave.cpp
diff --git a/src/LL/Asserv2/I2Cslave.hpp b/src/low_level/src/Asserv2/I2Cslave.hpp
similarity index 100%
rename from src/LL/Asserv2/I2Cslave.hpp
rename to src/low_level/src/Asserv2/I2Cslave.hpp
diff --git a/src/LL/Asserv2/VoltageReader.cpp b/src/low_level/src/Asserv2/VoltageReader.cpp
similarity index 100%
rename from src/LL/Asserv2/VoltageReader.cpp
rename to src/low_level/src/Asserv2/VoltageReader.cpp
diff --git a/src/LL/Asserv2/VoltageReader.hpp b/src/low_level/src/Asserv2/VoltageReader.hpp
similarity index 100%
rename from src/LL/Asserv2/VoltageReader.hpp
rename to src/low_level/src/Asserv2/VoltageReader.hpp
diff --git a/src/LL/Asserv2bazar/Asserv2bazar.ino b/src/low_level/src/Asserv2bazar/Asserv2bazar.ino
similarity index 100%
rename from src/LL/Asserv2bazar/Asserv2bazar.ino
rename to src/low_level/src/Asserv2bazar/Asserv2bazar.ino
diff --git a/src/LL/I2C_Scanner/I2C_Scanner.ino b/src/low_level/src/I2C_Scanner/I2C_Scanner.ino
similarity index 100%
rename from src/LL/I2C_Scanner/I2C_Scanner.ino
rename to src/low_level/src/I2C_Scanner/I2C_Scanner.ino
diff --git a/src/LL/Lecture_tension_I2C/Lecture_tension_I2C.ino b/src/low_level/src/Lecture_tension_I2C/Lecture_tension_I2C.ino
similarity index 100%
rename from src/LL/Lecture_tension_I2C/Lecture_tension_I2C.ino
rename to src/low_level/src/Lecture_tension_I2C/Lecture_tension_I2C.ino
diff --git a/src/LL/OLED_I2C/OLED_I2C.ino b/src/low_level/src/OLED_I2C/OLED_I2C.ino
similarity index 100%
rename from src/LL/OLED_I2C/OLED_I2C.ino
rename to src/low_level/src/OLED_I2C/OLED_I2C.ino
diff --git a/src/LL/SPI_master/SPI_master.ino b/src/low_level/src/SPI_master/SPI_master.ino
similarity index 100%
rename from src/LL/SPI_master/SPI_master.ino
rename to src/low_level/src/SPI_master/SPI_master.ino
diff --git a/src/LL/SPI_slave/SPI_slave.ino b/src/low_level/src/SPI_slave/SPI_slave.ino
similarity index 100%
rename from src/LL/SPI_slave/SPI_slave.ino
rename to src/low_level/src/SPI_slave/SPI_slave.ino
diff --git a/src/LL/ToF_I2C/ToF_I2C.ino b/src/low_level/src/ToF_I2C/ToF_I2C.ino
similarity index 100%
rename from src/LL/ToF_I2C/ToF_I2C.ino
rename to src/low_level/src/ToF_I2C/ToF_I2C.ino
diff --git a/src/LL/slaveI2C/slaveI2C.ino b/src/low_level/src/slaveI2C/slaveI2C.ino
similarity index 100%
rename from src/LL/slaveI2C/slaveI2C.ino
rename to src/low_level/src/slaveI2C/slaveI2C.ino
diff --git a/src/simulation/.python-version b/src/simulation/.python-version
new file mode 100644
index 00000000..24ee5b1b
--- /dev/null
+++ b/src/simulation/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/src/simulation/README.md b/src/simulation/README.md
new file mode 100644
index 00000000..e69de29b
diff --git a/src/simulation/pyproject.toml b/src/simulation/pyproject.toml
new file mode 100644
index 00000000..26e1e93e
--- /dev/null
+++ b/src/simulation/pyproject.toml
@@ -0,0 +1,24 @@
+[project]
+name = "simulation"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+authors = [
+ { name = "matthiasbienvenu", email = "matthias.bienvenu@telecom-sudparis.eu" }
+]
+requires-python = ">=3.12, <3.14"
+dependencies = [
+ "gymnasium>=1.2.3",
+ "matplotlib>=3.10.8",
+ "numpy>=2.4.1",
+ "onnx>=1.20.1",
+ "onnxruntime>=1.23.2",
+ "onnxscript>=0.5.7",
+ "psutil>=7.2.1",
+ "stable-baselines3[extra]>=2.7.1",
+ "torch>=2.9.1",
+]
+
+[build-system]
+requires = ["uv_build>=0.9.24,<0.10.0"]
+build-backend = "uv_build"
diff --git a/scripts/lanch_one_simu.py b/src/simulation/scripts/lanch_one_simu.py
similarity index 68%
rename from scripts/lanch_one_simu.py
rename to src/simulation/scripts/lanch_one_simu.py
index dd39739a..8779f0b1 100644
--- a/scripts/lanch_one_simu.py
+++ b/src/simulation/scripts/lanch_one_simu.py
@@ -1,34 +1,35 @@
+raise NotImplementedError("This file is currently begin worked on")
+
import os
import sys
-from typing import *
-import numpy as np
import onnxruntime as ort
-simu_path = __file__.rsplit('/', 2)[0] + '/src/Simulateur'
-if simu_path not in sys.path:
- sys.path.insert(0, simu_path)
+from simulation import (
+ VehicleEnv,
+)
+from simulation import config as c
+from utils import onnx_utils
-from onnx_utils import run_onnx_model
-from config import *
-from WebotsSimulationGymEnvironment import WebotsSimulationGymEnvironment
-from TemporalResNetExtractor import TemporalResNetExtractor
-from CNN1DResNetExtractor import CNN1DResNetExtractor
# -------------------------------------------------------------------------
-
# --- Chemin vers le fichier ONNX ---
ONNX_MODEL_PATH = "model.onnx"
+
# --- Initialisation du moteur d'inférence ONNX Runtime (ORT) ---
def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
if not os.path.exists(onnx_path):
- raise FileNotFoundError(f"Le fichier ONNX est introuvable à : {onnx_path}. Veuillez l'exporter d'abord.")
+ raise FileNotFoundError(
+ f"Le fichier ONNX est introuvable à : {onnx_path}. Veuillez l'exporter d'abord."
+ )
# Crée la session d'inférence
- return ort.InferenceSession(onnx_path) #On peut modifier le providers afin de mettre une CUDA
+ return ort.InferenceSession(
+ onnx_path
+ ) # On peut modifier le providers afin de mettre une CUDA
if __name__ == "__main__":
@@ -37,7 +38,6 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
-
# 2. Initialisation de la session ONNX Runtime
try:
ort_session = init_onnx_runtime_session(ONNX_MODEL_PATH)
@@ -48,11 +48,12 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
except FileNotFoundError as e:
print(f"ERREUR : {e}")
print(
- "Veuillez vous assurer que vous avez exécuté une fois le script d'entraînement pour exporter 'model.onnx'.")
+ "Veuillez vous assurer que vous avez exécuté une fois le script d'entraînement pour exporter 'model.onnx'."
+ )
sys.exit(1)
# 3. Boucle d'inférence (Test)
- env = WebotsSimulationGymEnvironment(0,0)
+ env = VehicleEnv(0, 0)
obs = env.reset()
print("Début de la simulation en mode inférence...")
@@ -60,8 +61,7 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
step_count = 0
while True:
-
- action = run_onnx_model(ort_session,obs)
+ action = onnx_utils.run_onnx_model(ort_session, obs)
# 4. Exécuter l'action dans l'environnement
obs, reward, done, info = env.step(action)
@@ -74,8 +74,6 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
print(f"Épisode(s) terminé(s) après {step_count} étapes.")
obs = env.reset()
-
-
# Fermeture propre (très important pour les processus parallèles SubprocVecEnv)
envs.close()
- print("Simulation terminée. Environnements fermés.")
\ No newline at end of file
+ print("Simulation terminée. Environnements fermés.")
diff --git a/src/simulation/scripts/launch_train_multiprocessing.py b/src/simulation/scripts/launch_train_multiprocessing.py
new file mode 100644
index 00000000..7b596176
--- /dev/null
+++ b/src/simulation/scripts/launch_train_multiprocessing.py
@@ -0,0 +1,115 @@
+import os
+from logging import DEBUG
+from typing import Any, Dict
+
+import torch.nn as nn
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import SubprocVecEnv
+
+from extractors import CNN1DResNetExtractor, TemporalResNetExtractor
+from simulation import VehicleEnv
+from simulation import config as c
+from utils import onnx_utils
+
+if __name__ == "__main__":
+ if not os.path.exists("/tmp/autotech/"):
+ os.mkdir("/tmp/autotech/")
+
+ os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
+
+ envs = SubprocVecEnv(
+ [
+ lambda sr=simulation_rank, vr=vehicle_rank: VehicleEnv(sr, vr)
+ for vehicle_rank in range(c.n_vehicles)
+ for simulation_rank in range(c.n_simulations)
+ ]
+ )
+
+ ExtractorClass = CNN1DResNetExtractor
+
+ policy_kwargs: Dict[str, Any] = dict(
+ features_extractor_class=ExtractorClass,
+ features_extractor_kwargs=dict(
+ context_size=c.context_size,
+ lidar_horizontal_resolution=c.lidar_horizontal_resolution,
+ camera_horizontal_resolution=c.camera_horizontal_resolution,
+ device=c.device,
+ ),
+ activation_fn=nn.ReLU,
+ net_arch=[512, 512, 512],
+ )
+
+ ppo_args: Dict[str, Any] = dict(
+ n_steps=4096,
+ n_epochs=10,
+ batch_size=256,
+ learning_rate=3e-4,
+ gamma=0.99,
+ verbose=1,
+ normalize_advantage=True,
+ device=c.device,
+ )
+
+ save_path = (
+ __file__.rsplit("/", 1)[0] + "/checkpoints/" + ExtractorClass.__name__ + "/"
+ )
+ os.makedirs(save_path, exist_ok=True)
+
+ print(save_path)
+ print(os.listdir(save_path))
+
+ valid_files = [x for x in os.listdir(save_path) if x.rstrip(".zip").isnumeric()]
+
+ if valid_files:
+ model_name = max(valid_files, key=lambda x: int(x.rstrip(".zip")))
+ print(f"Loading model {save_path + model_name}")
+ model = PPO.load(
+ save_path + model_name, envs, **ppo_args, policy_kwargs=policy_kwargs
+ )
+ i = int(model_name.rstrip(".zip")) + 1
+ print(f"----- Model found, loading {model_name} -----")
+
+ else:
+ model = PPO("MlpPolicy", envs, **ppo_args, policy_kwargs=policy_kwargs)
+
+ i = 0
+ print("----- Model not found, creating a new one -----")
+
+ print("MODEL HAS HYPER PARAMETERS:")
+ print(f"{model.learning_rate=}")
+ print(f"{model.gamma=}")
+ print(f"{model.verbose=}")
+ print(f"{model.n_steps=}")
+ print(f"{model.n_epochs=}")
+ print(f"{model.batch_size=}")
+ print(f"{model.device=}")
+
+ print("SERVER : finished executing")
+
+ # obs = envs.reset()
+ # while True:
+ # action, _states = model.predict(obs, deterministic=True) # Use deterministic=True for evaluation
+ # obs, reward, done, info = envs.step(action)
+ # envs.render() # Optional: visualize the environment
+
+ while True:
+ onnx_utils.export_onnx(model)
+ onnx_utils.test_onnx(model)
+
+ if c.LOG_LEVEL <= DEBUG:
+ # only used in debug mode
+ from utils import PlotModelIO
+
+ model.learn(
+ total_timesteps=500_000,
+ progress_bar=True,
+ callback=PlotModelIO(),
+ )
+ else:
+ model.learn(total_timesteps=500_000, progress_bar=True)
+
+ print("iteration over")
+ # TODO: we could just use a callback to save checkpoints or export the model to onnx
+ model.save(save_path + str(i))
+
+ i += 1
diff --git a/src/simulation/src/extractors/__init__.py b/src/simulation/src/extractors/__init__.py
new file mode 100644
index 00000000..35d70f1f
--- /dev/null
+++ b/src/simulation/src/extractors/__init__.py
@@ -0,0 +1,9 @@
+from .cnn_1d_extractor import CNN1DExtractor
+from .cnn_1d_resnet_extractor import CNN1DResNetExtractor
+from .temporal_resnet_extractor import TemporalResNetExtractor
+
+__all__ = [
+ "CNN1DExtractor",
+ "CNN1DResNetExtractor",
+ "TemporalResNetExtractor",
+]
diff --git a/src/Simulateur/CNN1DExtractor.py b/src/simulation/src/extractors/cnn_1d_extractor.py
similarity index 78%
rename from src/Simulateur/CNN1DExtractor.py
rename to src/simulation/src/extractors/cnn_1d_extractor.py
index e45f8a79..21841a14 100644
--- a/src/Simulateur/CNN1DExtractor.py
+++ b/src/simulation/src/extractors/cnn_1d_extractor.py
@@ -1,13 +1,18 @@
import torch
import torch.nn as nn
from gymnasium import spaces
-
-from stable_baselines3 import PPO
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
class CNN1DExtractor(BaseFeaturesExtractor):
- def __init__(self, space: spaces.Box, context_size: int, n_sensors: int, horizontal_resolution: int, device: str = "cpu"):
+ def __init__(
+ self,
+ space: spaces.Box,
+ context_size: int,
+ n_sensors: int,
+ horizontal_resolution: int,
+ device: str = "cpu",
+ ):
if context_size != 1:
raise ValueError("context_size must be 1 for CNN1DExtractor")
@@ -16,31 +21,25 @@ def __init__(self, space: spaces.Box, context_size: int, n_sensors: int, horizon
self.n_sensors = n_sensors
cnn = nn.Sequential(
# 1080
-
- nn.Conv1d(2, 64, kernel_size=7, stride=2, padding=3, device=device),
+ nn.Conv1d(2, 64, kernel_size=7, stride=2, padding=3, device=device),
nn.ReLU(),
nn.MaxPool1d(3),
nn.Dropout1d(0.2),
# 180
-
nn.Conv1d(64, 64, kernel_size=3, padding="same", device=device),
nn.ReLU(),
nn.MaxPool1d(3),
nn.Dropout1d(0.3),
# 60
-
nn.Conv1d(64, 128, kernel_size=3, padding="same", device=device),
nn.ReLU(),
-
nn.AvgPool1d(2),
nn.Dropout1d(0.4),
# 30
-
nn.Conv1d(128, 128, kernel_size=3, padding="same", device=device),
nn.ReLU(),
nn.AvgPool1d(2),
# 15
-
nn.Flatten(-2, -1),
nn.Dropout(0.5),
)
@@ -48,16 +47,17 @@ def __init__(self, space: spaces.Box, context_size: int, n_sensors: int, horizon
# Compute shape by doing one forward pass
with torch.no_grad():
n_flatten = cnn(
- torch.zeros([2, horizontal_resolution], dtype=torch.float32, device=device)
+ torch.zeros(
+ [2, horizontal_resolution], dtype=torch.float32, device=device
+ )
).shape[0]
super().__init__(space, n_flatten)
# we cannot assign this directly to self.cnn before calling the super constructor
self.net = cnn
-
def forward(self, observations: torch.Tensor) -> torch.Tensor:
# careful, this class takes as input the the sensor data AND the lidar data so is of size [batch_size, context_size, 1 + 1080 + 1080]
- observations = observations[:, 0, self.n_sensors:].reshape(-1, 2, 1080)
+ observations = observations[:, 0, self.n_sensors :].reshape(-1, 2, 1080)
# shape = [batch_size, 2, 1080]
return self.net(observations)
diff --git a/src/Simulateur/CNN1DResNetExtractor.py b/src/simulation/src/extractors/cnn_1d_resnet_extractor.py
similarity index 69%
rename from src/Simulateur/CNN1DResNetExtractor.py
rename to src/simulation/src/extractors/cnn_1d_resnet_extractor.py
index 8830a5c8..bf7899e2 100644
--- a/src/Simulateur/CNN1DResNetExtractor.py
+++ b/src/simulation/src/extractors/cnn_1d_resnet_extractor.py
@@ -1,8 +1,6 @@
import torch
import torch.nn as nn
-import torch.nn.functional as F
from gymnasium import spaces
-
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
@@ -16,36 +14,54 @@ def __init__(self, device: str = "cpu"):
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x[:, :, 0]
- #print(x.shape, x.mean(dim=2), x[0, 0])
+ # print(x.shape, x.mean(dim=2), x[0, 0])
x = self.conv(x)
x = self.dropout(x)
x = self.pool(x)
return x
-
class ResidualBlock(nn.Module):
"""
basic block with a residual connection
"""
- def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, device: str = "cpu"):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ downsample: bool = False,
+ device: str = "cpu",
+ ):
super().__init__()
if downsample:
stride = 2
- self.downsample = nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=2, device=device)
+ self.downsample = nn.Conv1d(
+ in_channels, out_channels, kernel_size=1, stride=2, device=device
+ )
elif in_channels == out_channels:
stride = 1
self.downsample = nn.Identity()
else:
stride = 1
- self.downsample = nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=1, device=device)
+ self.downsample = nn.Conv1d(
+ in_channels, out_channels, kernel_size=1, stride=1, device=device
+ )
self.bn1 = nn.BatchNorm1d(in_channels, device=device)
- self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, device=device)
+ self.conv1 = nn.Conv1d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ device=device,
+ )
self.bn2 = nn.BatchNorm1d(out_channels, device=device)
- self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, padding=1, device=device)
+ self.conv2 = nn.Conv1d(
+ out_channels, out_channels, kernel_size=3, padding=1, device=device
+ )
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout1d(0.4)
@@ -65,12 +81,23 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
return y
-
-
class CNN1DResNetExtractor(BaseFeaturesExtractor):
- def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolution: int, camera_horizontal_resolution: int, device: str = "cpu"):
- if (context_size, lidar_horizontal_resolution, camera_horizontal_resolution) != (1,) + 2*(1024,):
- raise ValueError("context_size must be 1 and lidar_horizontal_resolution and camera_horizontal_resolution must be 1024 for TemporalResNetExtractor")
+ def __init__(
+ self,
+ space: spaces.Box,
+ context_size: int,
+ lidar_horizontal_resolution: int,
+ camera_horizontal_resolution: int,
+ device: str = "cpu",
+ ):
+ if (
+ context_size,
+ lidar_horizontal_resolution,
+ camera_horizontal_resolution,
+ ) != (1,) + 2 * (1024,):
+ raise ValueError(
+ "context_size must be 1 and lidar_horizontal_resolution and camera_horizontal_resolution must be 1024 for TemporalResNetExtractor"
+ )
self.lidar_horizontal_resolution = lidar_horizontal_resolution
self.camera_horizontal_resolution = camera_horizontal_resolution
@@ -79,35 +106,28 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
# shape = [batch_size, 2, 1024]
Compressor(device),
# shape = [batch_size, 64, 256]
-
ResidualBlock(64, 64, device=device),
ResidualBlock(64, 64, device=device),
ResidualBlock(64, 64, downsample=True, device=device),
# shape = [batch_size, 128, 128]
-
ResidualBlock(64, 64, device=device),
ResidualBlock(64, 64, device=device),
ResidualBlock(64, 128, downsample=True, device=device),
# shape = [batch_size, 128, 64]
-
ResidualBlock(128, 128, device=device),
ResidualBlock(128, 128, device=device),
ResidualBlock(128, 128, downsample=True, device=device),
# shape = [batch_size, 256, 32]
-
ResidualBlock(128, 128, device=device),
ResidualBlock(128, 128, device=device),
ResidualBlock(128, 256, downsample=True, device=device),
# shape = [batch_size, 256, 16]
-
ResidualBlock(256, 256, device=device),
ResidualBlock(256, 256, device=device),
ResidualBlock(256, 256, downsample=True, device=device),
# shape = [batch_size, 512, 8]
-
nn.AvgPool1d(8),
# shape = [batch_size, 512, 1]
-
nn.Flatten(),
# shape = [batch_size, 256]
)
@@ -115,7 +135,11 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
# Compute shape by doing one forward pass
with torch.no_grad():
n_flatten = net(
- torch.zeros([1, 2, context_size, lidar_horizontal_resolution], dtype=torch.float32, device=device)
+ torch.zeros(
+ [1, 2, context_size, lidar_horizontal_resolution],
+ dtype=torch.float32,
+ device=device,
+ )
).shape[1]
print("n_flatten: ", n_flatten)
super().__init__(space, n_flatten)
@@ -123,7 +147,6 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
# we cannot assign this directly to self.cnn before calling the super constructor
self.net = net
-
def forward(self, observations: torch.Tensor) -> torch.Tensor:
extracted = self.net(observations)
diff --git a/src/Simulateur/TemporalResNetExtractor.py b/src/simulation/src/extractors/temporal_resnet_extractor.py
similarity index 72%
rename from src/Simulateur/TemporalResNetExtractor.py
rename to src/simulation/src/extractors/temporal_resnet_extractor.py
index 117fbb66..439dc176 100644
--- a/src/Simulateur/TemporalResNetExtractor.py
+++ b/src/simulation/src/extractors/temporal_resnet_extractor.py
@@ -1,8 +1,6 @@
import torch
import torch.nn as nn
-import torch.nn.functional as F
from gymnasium import spaces
-
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
@@ -25,30 +23,47 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
return x
-
-
class ResidualBlock(nn.Module):
"""
basic block with a residual connection
"""
- def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, device: str = "cpu"):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ downsample: bool = False,
+ device: str = "cpu",
+ ):
super().__init__()
if downsample:
stride = 2
- self.downsample = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=2, device=device)
+ self.downsample = nn.Conv2d(
+ in_channels, out_channels, kernel_size=1, stride=2, device=device
+ )
elif in_channels == out_channels:
stride = 1
self.downsample = nn.Identity()
else:
stride = 1
- self.downsample = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, device=device)
+ self.downsample = nn.Conv2d(
+ in_channels, out_channels, kernel_size=1, stride=1, device=device
+ )
self.bn1 = nn.BatchNorm2d(in_channels, device=device)
- self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, device=device)
+ self.conv1 = nn.Conv2d(
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ device=device,
+ )
self.bn2 = nn.BatchNorm2d(out_channels, device=device)
- self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, device=device)
+ self.conv2 = nn.Conv2d(
+ out_channels, out_channels, kernel_size=3, padding=1, device=device
+ )
self.relu = nn.ReLU(inplace=True)
self.dropout = nn.Dropout2d(0.5)
@@ -68,11 +83,20 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
return y
-
-
class TemporalResNetExtractor(BaseFeaturesExtractor):
- def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolution: int, camera_horizontal_resolution: int, device: str = "cpu"):
- if (context_size, lidar_horizontal_resolution, camera_horizontal_resolution) != 3*(128,):
+ def __init__(
+ self,
+ space: spaces.Box,
+ context_size: int,
+ lidar_horizontal_resolution: int,
+ camera_horizontal_resolution: int,
+ device: str = "cpu",
+ ):
+ if (
+ context_size,
+ lidar_horizontal_resolution,
+ camera_horizontal_resolution,
+ ) != 3 * (128,):
raise ValueError("context_size must be 128 for TemporalResNetExtractor")
self.lidar_horizontal_resolution = lidar_horizontal_resolution
@@ -81,26 +105,20 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
net = nn.Sequential(
Compressor(device),
# shape = [batch_size, 64, 32, 32]
-
ResidualBlock(64, 64, device=device),
ResidualBlock(64, 64, device=device),
# shape = [batch_size, 64, 32, 32]
-
ResidualBlock(64, 128, downsample=True, device=device),
ResidualBlock(128, 128, device=device),
# shape = [batch_size, 128, 16, 16]
-
ResidualBlock(128, 256, downsample=True, device=device),
ResidualBlock(256, 256, device=device),
# shape = [batch_size, 256, 8, 8]
-
ResidualBlock(256, 512, downsample=True, device=device),
ResidualBlock(512, 512, device=device),
# shape = [batch_size, 512, 4, 4]
-
nn.AvgPool2d(4),
# shape = [batch_size, 512, 1, 1]
-
nn.Flatten(),
# shape = [batch_size, 256]
)
@@ -108,7 +126,9 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
# Compute shape by doing one forward pass
with torch.no_grad():
n_flatten = net(
- torch.zeros([1, 2, context_size, lidar_horizontal_resolution], device=device)
+ torch.zeros(
+ [1, 2, context_size, lidar_horizontal_resolution], device=device
+ )
).shape[1]
print("n_flatten: ", n_flatten)
super().__init__(space, n_flatten)
@@ -116,7 +136,6 @@ def __init__(self, space: spaces.Box, context_size: int, lidar_horizontal_resolu
# we cannot assign this directly to self.net before calling the super constructor
self.net = net
-
def forward(self, observations: torch.Tensor) -> torch.Tensor:
extracted = self.net(observations)
diff --git a/src/simulation/src/simulation/__init__.py b/src/simulation/src/simulation/__init__.py
new file mode 100644
index 00000000..7f8f7a6b
--- /dev/null
+++ b/src/simulation/src/simulation/__init__.py
@@ -0,0 +1,3 @@
+from .vehicle_env import VehicleEnv
+
+__all__ = ["VehicleEnv"]
diff --git a/src/Simulateur/config.py b/src/simulation/src/simulation/config.py
similarity index 59%
rename from src/Simulateur/config.py
rename to src/simulation/src/simulation/config.py
index 6db6d990..93339898 100644
--- a/src/Simulateur/config.py
+++ b/src/simulation/src/simulation/config.py
@@ -1,7 +1,7 @@
# just a file that lets us define some constants that are used in multiple files the simulation
-from torch.cuda import is_available
import logging
+from torch.cuda import is_available
n_map = 2
n_simulations = 2
@@ -14,8 +14,8 @@
device = "cuda" if is_available() else "cpu"
context_size = 1
-lidar_horizontal_resolution = 1024 # DON'T CHANGE THIS VALUE PLS
-camera_horizontal_resolution = 1024 # DON'T CHANGE THIS VALUE PLS
+lidar_horizontal_resolution = 1024 # DON'T CHANGE THIS VALUE PLS
+camera_horizontal_resolution = 1024 # DON'T CHANGE THIS VALUE PLS
-LOG_LEVEL = logging.DEBUG
-FORMATTER = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+LOG_LEVEL = logging.INFO
+FORMATTER = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
diff --git a/src/simulation/src/simulation/py.typed b/src/simulation/src/simulation/py.typed
new file mode 100644
index 00000000..e69de29b
diff --git a/src/simulation/src/simulation/vehicle_env.py b/src/simulation/src/simulation/vehicle_env.py
new file mode 100644
index 00000000..1ff28def
--- /dev/null
+++ b/src/simulation/src/simulation/vehicle_env.py
@@ -0,0 +1,169 @@
+import logging
+import os
+import subprocess
+from typing import Dict, Tuple
+
+import gymnasium as gym
+import numpy as np
+
+from webots import webots_path
+
+from . import config as c
+
+ObsType = np.ndarray # float32 array
+ActType = np.ndarray # int64 array
+
+
+class VehicleEnv(gym.Env):
+ """
+ Gymnasium environment representing a single vehicle controlled inside
+ a Webots simulation.
+
+ Creating an instance of this environment sets up all required IPC
+ mechanisms (named pipes) and may spawn a new Webots process, depending
+ on the vehicle index.
+
+ Communication is organized as a closed control loop:
+
+ VehicleEnv
+ |
+ | action
+ ▼
+ controller_vehicle_driver
+ |
+ | observation
+ ▼
+ controller_world_supervisor (shared by vehicle of the same simulation)
+ |
+ | observation, reward, done, truncated
+ ▼
+ VehicleEnv
+ """
+
+ def __init__(self, simulation_rank: int, vehicle_rank: int):
+ super().__init__()
+ self.simulation_rank = simulation_rank
+ self.vehicle_rank = vehicle_rank
+
+ self.handler = logging.FileHandler(
+ f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.vehicle_rank}.log"
+ )
+ self.handler.setFormatter(c.FORMATTER)
+ self.log = logging.getLogger(
+ f"SERVER_{self.simulation_rank}_{self.vehicle_rank}"
+ )
+ self.log.setLevel(level=c.LOG_LEVEL)
+ self.log.addHandler(self.handler)
+
+ self.log.info("Initialisation started")
+
+ # this is only true if lidar_horizontal_resolution = camera_horizontal_resolution
+ box_min = np.zeros(
+ [2, c.context_size, c.lidar_horizontal_resolution], dtype=np.float32
+ )
+ box_max = (
+ np.ones(
+ [2, c.context_size, c.lidar_horizontal_resolution], dtype=np.float32
+ )
+ * 30
+ )
+
+ self.observation_space = gym.spaces.Box(box_min, box_max, dtype=np.float32)
+ self.action_space = gym.spaces.MultiDiscrete(
+ [c.n_actions_steering, c.n_actions_speed]
+ )
+
+ if not os.path.exists("/tmp/autotech"):
+ os.mkdir("/tmp/autotech")
+
+ self.log.debug("Creation of the pipes")
+
+ os.mkfifo(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe")
+ os.mkfifo(f"/tmp/autotech/serverto{simulation_rank}_{vehicle_rank}.pipe")
+ os.mkfifo(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}tosupervisor.pipe")
+
+ # --mode=fast --minimize --no-rendering --batch --stdout
+ if vehicle_rank == 0:
+ proc = subprocess.Popen(
+ [
+ "webots",
+ f"{webots_path}/worlds/piste{simulation_rank % c.n_map}.wbt",
+ "--mode=fast",
+ "--minimize",
+ "--batch",
+ "--stdout",
+ ]
+ )
+
+ with open("/tmp/autotech/simulationranks", "a") as f:
+ f.write(f"{proc.pid} {simulation_rank}_{vehicle_rank}\n")
+
+ self.log.debug("Connection to the vehicle")
+ self.fifo_w = open(
+ f"/tmp/autotech/serverto{simulation_rank}_{vehicle_rank}.pipe", "wb"
+ )
+ self.log.debug("Connection to the supervisor")
+ self.fifo_r = open(
+ f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe", "rb"
+ )
+
+ self.log.info("Initialisation finished\n")
+
+ def reset(self, seed: int | None = None, **options) -> Tuple[ObsType, Dict]:
+ # basically useless function
+
+ # lidar data
+ # this is true for lidar_horizontal_resolution = camera_horizontal_resolution
+ self.context = obs = np.zeros(
+ [2, c.context_size, c.lidar_horizontal_resolution], dtype=np.float32
+ )
+ info = {}
+ self.log.info("reset finished\n")
+ return obs, info
+
+ def step(self, action: ActType):
+ self.log.info("Starting step")
+ self.log.info(f"sending {action=}")
+ self.fifo_w.write(action.tobytes())
+ self.fifo_w.flush()
+
+ # communication with the supervisor
+ self.log.debug("trying to get info from supervisor")
+ cur_state = np.frombuffer(
+ self.fifo_r.read(
+ np.dtype(np.float32).itemsize
+ * (
+ c.n_sensors
+ + c.lidar_horizontal_resolution
+ + c.camera_horizontal_resolution
+ )
+ ),
+ dtype=np.float32,
+ )
+ self.log.info(f"received {cur_state=}")
+ reward = np.frombuffer(
+ self.fifo_r.read(np.dtype(np.float32).itemsize), dtype=np.float32
+ )[0] # scalar
+ self.log.info(f"received {reward=}")
+ done = np.frombuffer(
+ self.fifo_r.read(np.dtype(np.bool).itemsize), dtype=np.bool
+ )[0] # scalar
+ self.log.info(f"received {done=}")
+ truncated = np.frombuffer(
+ self.fifo_r.read(np.dtype(np.bool).itemsize), dtype=np.bool
+ )[0] # scalar
+ self.log.info(f"received {truncated=}")
+ info = {}
+
+ cur_state = np.nan_to_num(cur_state[c.n_sensors :], nan=0.0, posinf=30.0)
+
+ lidar_obs = cur_state[: c.lidar_horizontal_resolution]
+ camera_obs = cur_state[c.lidar_horizontal_resolution :]
+
+ self.context = obs = np.concatenate(
+ [self.context[:, 1:], [lidar_obs[None], camera_obs[None]]], axis=1
+ )
+
+ self.log.info("step over")
+
+ return obs, reward, done, truncated, info
diff --git a/src/simulation/src/utils/__init__.py b/src/simulation/src/utils/__init__.py
new file mode 100644
index 00000000..4b7ee72c
--- /dev/null
+++ b/src/simulation/src/utils/__init__.py
@@ -0,0 +1,3 @@
+from .plot_model_io import PlotModelIO
+
+__all__ = ["PlotModelIO"]
diff --git a/src/Simulateur/onnx_utils.py b/src/simulation/src/utils/onnx_utils.py
similarity index 74%
rename from src/Simulateur/onnx_utils.py
rename to src/simulation/src/utils/onnx_utils.py
index 7dd7dd5b..0e58bcd9 100644
--- a/src/Simulateur/onnx_utils.py
+++ b/src/simulation/src/utils/onnx_utils.py
@@ -1,20 +1,16 @@
-import os
-from stable_baselines3 import PPO
-import torch.nn as nn
-import torch
-from config import *
import numpy as np
-from CNN1DExtractor import CNN1DExtractor
-from TemporalResNetExtractor import TemporalResNetExtractor
-
import onnxruntime as ort
+import torch
+import torch.nn as nn
+
+from simulation import config as c
def get_true_model(model):
return nn.Sequential(
model.policy.features_extractor.net,
model.policy.mlp_extractor.policy_net,
- model.policy.action_net
+ model.policy.action_net,
).to("cpu")
@@ -22,33 +18,34 @@ def export_onnx(model):
model.policy.eval()
device = model.policy.device
true_model = get_true_model(model)
- x = torch.randn(1, 2, context_size, lidar_horizontal_resolution)
+ x = torch.randn(1, 2, c.context_size, c.lidar_horizontal_resolution)
with torch.no_grad():
torch.onnx.export(
true_model,
- x,
+ x, # type: ignore
"model.onnx",
input_names=["input"],
output_names=["output"],
- dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}
+ dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
)
true_model.to(device)
model.policy.to(device)
model.policy.train()
-def run_onnx_model(session : ort.InferenceSession,x : np.ndarray):
+def run_onnx_model(session: ort.InferenceSession, x: np.ndarray):
return session.run(None, {"input": x})[0]
+
def test_onnx(model):
device = model.policy.device
model.policy.eval()
true_model = get_true_model(model)
loss_fn = nn.MSELoss()
- x = torch.randn(1000, 2, context_size, lidar_horizontal_resolution)
+ x = torch.randn(1000, 2, c.context_size, c.lidar_horizontal_resolution)
try:
session = ort.InferenceSession("model.onnx")
@@ -63,7 +60,7 @@ def test_onnx(model):
y_true_train = true_model(x)
true_model.eval()
- y_onnx = run_onnx_model(session,x.cpu().numpy())
+ y_onnx = run_onnx_model(session, x.cpu().numpy())
loss_test = loss_fn(y_true_test, torch.tensor(y_onnx))
loss_train = loss_fn(y_true_train, torch.tensor(y_onnx))
@@ -73,4 +70,3 @@ def test_onnx(model):
true_model.to(device)
model.policy.to(device)
model.policy.train()
-
diff --git a/src/simulation/src/utils/plot_model_io.py b/src/simulation/src/utils/plot_model_io.py
new file mode 100644
index 00000000..ac800fcf
--- /dev/null
+++ b/src/simulation/src/utils/plot_model_io.py
@@ -0,0 +1,108 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from stable_baselines3.common.callbacks import BaseCallback
+
+from simulation import config as c
+
+steering_true = (c.n_actions_steering + 1) // 2
+
+
+class PlotModelIO(BaseCallback):
+ """
+ A stable_baselines3 callback used to plot live visualizations of model inputs and outputs.
+ """
+
+ def __init__(self, verbose=0):
+ super().__init__(verbose)
+ self.fig, self.ax = plt.subplots(4, 1, figsize=(10, 8))
+
+ # Steering bars
+ self.steering_bars = self.ax[0].bar(
+ range(c.n_actions_steering), np.zeros(c.n_actions_steering), color="blue"
+ )
+ self.steering_avg = [
+ self.ax[0].plot(
+ [0, 0], [0, 1], color=(i / 3, 1 - i / 3, 0), label="Average"
+ )[0]
+ for i in range(4)
+ ]
+ self.ax[0].set_ylim(0, 1) # Probabilities range from 0 to 1
+ self.ax[0].set_title("Steering Action Probabilities")
+
+ # Speed bars
+ self.speed_bars = self.ax[1].bar(
+ range(c.n_actions_speed), np.zeros(c.n_actions_speed), color="blue"
+ )
+ self.speed_avg = self.ax[1].plot([0, 0], [0, 1], color="red", label="Average")[
+ 0
+ ]
+ self.ax[1].set_ylim(0, 1) # Probabilities range from 0 to 1
+ self.ax[1].set_title("Speed Action Probabilities")
+
+ # LiDAR img
+ self.lidar_img = self.ax[2].imshow(
+ np.zeros((c.lidar_horizontal_resolution, c.lidar_horizontal_resolution)),
+ cmap="gray",
+ vmin=0,
+ vmax=np.log(31),
+ )
+ self.ax[2].set_title("LiDAR Image")
+
+ # Camera img
+ self.camera_img = self.ax[3].imshow(
+ np.zeros(
+ (c.camera_horizontal_resolution, c.camera_horizontal_resolution, 3)
+ ),
+ cmap="RdYlGn",
+ vmin=-1,
+ vmax=1,
+ )
+ self.ax[3].set_title("Camera Image")
+
+ def _on_step(self) -> bool:
+ global steering_true
+
+ obs = self.locals["obs_tensor"].clone().detach()
+
+ self.lidar_img.set_array(np.log(1 + obs[0, 0, :, :].cpu().numpy()))
+ self.camera_img.set_array(obs[0, 1, :, :].cpu().numpy())
+ with torch.no_grad():
+ latent = self.model.policy.features_extractor(obs)
+ extracted = self.model.policy.mlp_extractor.policy_net(latent)
+ output = self.model.policy.action_net(extracted)[0]
+
+ # Test different temperatures
+ T = [0.4, 0.6, 0.8, 1.0]
+ steer_probs = torch.softmax(output[: c.n_actions_steering], dim=-1).to(
+ "cpu"
+ )
+ speed_probs = torch.softmax(output[c.n_actions_steering :], dim=-1).to(
+ "cpu"
+ )
+
+ # Update the probabilities
+ for i, bar in enumerate(self.steering_bars):
+ bar.set_height(steer_probs[i].item())
+
+ for i in range(4):
+ steer_probs_T = torch.softmax(
+ output[: c.n_actions_steering] / T[i], dim=-1
+ ).to("cpu")
+ steering_avg = (
+ (steer_probs_T / steer_probs_T.sum() * torch.arange(16))
+ .sum()
+ .item()
+ )
+ self.steering_avg[i].set_xdata([steering_avg, steering_avg])
+
+ for i, bar in enumerate(self.speed_bars):
+ bar.set_height(speed_probs[i].item())
+
+ speed_avg = (speed_probs * torch.arange(c.n_actions_speed)).sum().item()
+ self.speed_avg.set_xdata([speed_avg, speed_avg])
+
+ plt.draw()
+ plt.pause(1e-8)
+
+ return True # Continue training
diff --git a/src/simulation/src/utils/simulation_rank.py b/src/simulation/src/utils/simulation_rank.py
new file mode 100644
index 00000000..fb6e9ef3
--- /dev/null
+++ b/src/simulation/src/utils/simulation_rank.py
@@ -0,0 +1,47 @@
+import os
+import re
+
+import psutil
+
+import __main__
+from webots import webots_path
+
+
+def get_simulation_rank():
+ """
+ This function has to be ran from a webots controller.
+ Get the simulation rank of the current python process.
+ """
+
+ # check if we are in a controller
+ if (
+ result := re.match(
+ os.path.join(
+ webots_path, r"controllers/controller(\w+)/controller(\w+)\.py"
+ ),
+ __main__.__file__,
+ )
+ ) is None or result.group(1) != result.group(2):
+ raise ValueError(
+ "get_simulation_rank has to be called inside a webots controller"
+ )
+
+ # webots precess launches webots-bin internally who is the process that launches the controllers
+ # that's why we need to get the pppid
+ proc = psutil.Process(os.getpid())
+ parent = proc.parent()
+ assert parent is not None
+ grandparent = parent.parent()
+ assert grandparent is not None
+ pppid = str(grandparent.pid)
+
+ # Simulation rank
+ with open("/tmp/autotech/simulationranks", "r") as f:
+ matches = re.search(
+ pppid + r" (\d+)",
+ f.read(),
+ re.MULTILINE,
+ )
+ assert matches is not None
+
+ return int(matches.group(1))
diff --git a/src/simulation/src/webots/__init__.py b/src/simulation/src/webots/__init__.py
new file mode 100644
index 00000000..76248e7b
--- /dev/null
+++ b/src/simulation/src/webots/__init__.py
@@ -0,0 +1,5 @@
+import os
+
+webots_path = os.path.dirname(__file__)
+
+__all__ = ["webots_path"]
diff --git a/src/Simulateur/controllers/controller_jaune/controller_jaune.py b/src/simulation/src/webots/controllers/controller_jaune/controller_jaune.py
similarity index 100%
rename from src/Simulateur/controllers/controller_jaune/controller_jaune.py
rename to src/simulation/src/webots/controllers/controller_jaune/controller_jaune.py
diff --git a/src/simulation/src/webots/controllers/controller_vehicle_driver/controller_vehicle_driver.py b/src/simulation/src/webots/controllers/controller_vehicle_driver/controller_vehicle_driver.py
new file mode 100644
index 00000000..a9d4e6f9
--- /dev/null
+++ b/src/simulation/src/webots/controllers/controller_vehicle_driver/controller_vehicle_driver.py
@@ -0,0 +1,212 @@
+import logging
+from enum import Enum, auto
+from typing import Tuple, cast
+
+import numpy as np
+from controller import Camera, Lidar, TouchSensor
+from vehicle import Driver
+
+from simulation import config as c
+from utils.simulation_rank import get_simulation_rank
+
+
+def too_close(lidar, dir):
+ R = 0.83
+ length = len(lidar)
+ straight = lidar[length // 2]
+ if dir:
+ nearest = min(lidar[length // 2 :])
+ else:
+ nearest = min(lidar[: length // 2])
+
+ cos = nearest / straight
+
+ # I don't know why sometimes this happens
+ if cos < -1 or cos > 1:
+ return True
+
+ theta = np.arccos(cos)
+ L = R * (1 - np.sin(theta))
+ return nearest < L
+
+
+class State(Enum):
+ AI = auto()
+ BACK = auto()
+
+
+class VehicleDriver(Driver):
+ """
+ This class is a subclass of the Driver class and is used to control the vehicle.
+ It basically receives instructions from the controller_world_supervisor and follows them.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self.state = State.AI
+
+ basicTimeStep = int(self.getBasicTimeStep())
+ self.sensorTime = basicTimeStep // 4
+
+ self.v_min = 1
+ self.v_max = 9
+
+ self.vehicle_rank = int(self.getName().split("_")[-1])
+ # Lidar
+ self.lidar = cast(Lidar, self.getDevice("Hokuyo"))
+ self.lidar.enable(self.sensorTime)
+ self.lidar.enablePointCloud()
+
+ # Camera
+ self.camera = cast(Camera, self.getDevice("RASPI_Camera_V2"))
+ self.camera.enable(self.sensorTime)
+
+ # Checkpoint sensor
+ self.touch_sensor = cast(TouchSensor, self.getDevice("touch_sensor"))
+ self.touch_sensor.enable(self.sensorTime)
+
+ self.simulation_rank = get_simulation_rank()
+
+ # Logger
+ self.handler = logging.FileHandler(
+ f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.vehicle_rank}.log"
+ )
+ self.handler.setFormatter(c.FORMATTER)
+ self.log = logging.getLogger(
+ f"CLIENT_{self.simulation_rank}_{self.vehicle_rank}"
+ )
+ self.log.setLevel(level=c.LOG_LEVEL)
+ self.log.addHandler(self.handler)
+
+ self.log.debug("Connection to the server")
+ self.fifo_r = open(
+ f"/tmp/autotech/serverto{self.simulation_rank}_{self.vehicle_rank}.pipe",
+ "rb",
+ )
+ self.log.debug("Connection with the supervisor")
+ self.fifo_w = open(
+ f"/tmp/autotech/{self.simulation_rank}_{self.vehicle_rank}tosupervisor.pipe",
+ "wb",
+ )
+
+ # Vérification de l"état de la voiture
+ def observe(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+ # try:
+ sensor_data = np.array([self.touch_sensor.getValue()], dtype=np.float32)
+
+ lidar_data = np.array(self.lidar.getRangeImage(), dtype=np.float32)
+
+ camera_data = np.array(self.camera.getImageArray(), dtype=np.float32)
+ # shape = (1080, 1, 3)
+ camera_data = camera_data.transpose(1, 2, 0)[0]
+ # shape = (3, 1080)
+ color = np.argmax(camera_data, axis=0)
+ camera_data = (
+ (color == 0).astype(np.float32) * -1
+ + (color == 1).astype(np.float32) * 1
+ + (color == 2).astype(np.float32) * 0
+ )
+ # red -> -1
+ # green -> 1
+ # blue -> 0
+
+ return (sensor_data, lidar_data, camera_data)
+
+ # except:
+ # # En cas de non retour lidar
+ # return (
+ # np.array(self.touch_sensor.getValue(), dtype=np.float32),
+ # np.zeros(self.lidar.getNumberOfPoints(), dtype=np.float32),
+ # )
+
+ # Fonction step de l"environnement GYM
+ def step(self):
+ match self.state:
+ case State.AI:
+ self.ai()
+ case State.BACK:
+ # TODO: this is a very poor fix and not a definitive solution
+ # we HAVE to remove this line and properly manage the communication
+ # in State.BACK so that the ai doesn't train the data during that
+ # period to make sure that the training is not polluted
+ self.ai()
+
+ self.back()
+
+ return super().step()
+
+ def ai(self):
+ self.log.info("Starting step")
+ obs = self.observe()
+ self.log.info(f"Observe {obs=}")
+ self.fifo_w.write(np.concatenate(obs).tobytes())
+ self.fifo_w.flush()
+
+ self.log.debug("Trying to read action from the server")
+ action = np.frombuffer(
+ self.fifo_r.read(np.dtype(np.int64).itemsize * 2), dtype=np.int64
+ )
+ self.log.info(f"received {action=}")
+
+ # Simulation step
+
+ action_steering = np.linspace(
+ -0.4, 0.4, c.n_actions_steering, dtype=np.float32
+ )[action[0]]
+
+ action_speed = np.linspace(
+ self.v_min, self.v_max, c.n_actions_speed, dtype=np.float32
+ )[action[1]]
+
+ cur_angle = self.getSteeringAngle()
+ dt = self.getBasicTimeStep()
+ omega = 20 # rad/s (max angular speed of the steering servo)
+
+ action_steering = cur_angle + np.clip(
+ action_steering - cur_angle, -omega * dt, omega * dt
+ )
+
+ self.setSteeringAngle(float(action_steering))
+ self.setCruisingSpeed(float(action_speed))
+
+ if self.touch_sensor.getValue():
+ self.state = State.BACK
+
+ def back(self):
+ # si mur de "dir": braquer à "dir"" et reculer jusqu'à pouvoir réavancer (distance au mur à vérif)
+ lidar, cam = self.observe()[1:]
+ S = sum(cam)
+ dir = S > 0
+ if dir:
+ self.setSteeringAngle(0.33)
+ if too_close(lidar, dir):
+ self.setCruisingSpeed(-2)
+ else:
+ self.state = State.AI
+ else:
+ self.setSteeringAngle(-0.33)
+ if too_close(lidar, dir):
+ self.setCruisingSpeed(-2)
+ else:
+ self.state = State.AI
+
+ def run(self):
+ # this call is just there to make sure at least one step
+ # is done in the entire simulation before we call lidar.getRangeImage()
+ # otherwise it will crash the controller with the message:
+ # WARNING: 'controller_vehicle_driver' controller crashed.
+ # WARNING: controller_vehicle_driver: The process crashed some time after starting successfully.
+ super().step()
+ while self.step() != -1:
+ pass
+
+
+# ----------------Programme principal--------------------
+def main():
+ driver = VehicleDriver()
+ driver.log.info("Starting the vehicle driver")
+ driver.run()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/Simulateur/controllers/controller_violet/controller_violet.py b/src/simulation/src/webots/controllers/controller_violet/controller_violet.py
similarity index 77%
rename from src/Simulateur/controllers/controller_violet/controller_violet.py
rename to src/simulation/src/webots/controllers/controller_violet/controller_violet.py
index 171fd89e..6fd310de 100644
--- a/src/Simulateur/controllers/controller_violet/controller_violet.py
+++ b/src/simulation/src/webots/controllers/controller_violet/controller_violet.py
@@ -7,9 +7,8 @@
import numpy as np
-from vehicle import Driver
from controller import Lidar
-
+from vehicle import Driver
driver = Driver()
@@ -28,14 +27,14 @@
# vitesse en km/h
speed = 0
-maxSpeed = 28 #km/h
+maxSpeed = 28 # km/h
# angle de la direction
angle = 0
-maxangle = 0.28 #rad (étrange, la voiture est défini pour une limite à 0.31 rad...
+maxangle = 0.28 # rad (étrange, la voiture est défini pour une limite à 0.31 rad...
-backwards_duration = 2000 # ms
-stop_duration = 3000 # ms
+backwards_duration = 2000 # ms
+stop_duration = 3000 # ms
death_count = 0
@@ -49,7 +48,7 @@ def backwards(lidar_data, camera_data):
speed = -1
avg_color = np.mean(camera_data, axis=0) / 255
- angle = -0.5*avg_color[0] + 0.5*avg_color[1]
+ angle = -0.5 * avg_color[0] + 0.5 * avg_color[1]
driver.setCruisingSpeed(speed)
driver.setSteeringAngle(angle)
@@ -63,17 +62,18 @@ def backwards(lidar_data, camera_data):
driver.setSteeringAngle(angle)
driver.step()
+
def stop():
driver.setCruisingSpeed(0)
driver.setSteeringAngle(0)
for _ in range(stop_duration // basicTimeStep):
driver.step()
- # will be reset by the controllerWorldSupervisor.py
+ # will be reset by the controller_world_supervisor.py
while driver.step() != -1:
- lidar_data = np.nan_to_num(lidar.getRangeImage(), nan=0., posinf=30.)
- camera_data = np.nan_to_num(camera.getImageArray(), nan=0., posinf=30.).squeeze()
+ lidar_data = np.nan_to_num(lidar.getRangeImage(), nan=0.0, posinf=30.0)
+ camera_data = np.nan_to_num(camera.getImageArray(), nan=0.0, posinf=30.0).squeeze()
sensor_data = touch_sensor.getValue()
# goes backwards
@@ -87,17 +87,16 @@ def stop():
death_count = 0
stop()
-
- speed = 1 #km/h
- #l'angle de la direction est la différence entre les mesures des rayons
+ speed = 1 # km/h
+ # l'angle de la direction est la différence entre les mesures des rayons
avg_color = np.mean(camera_data, axis=0) / 255
i = np.argmin(lidar_data)
m = lidar_data[i]
- if m <= .7:
+ if m <= 0.7:
angle = 0.3 if i <= 64 else -0.3
else:
- angle = 0.3*avg_color[0] - 0.3*avg_color[1]
+ angle = 0.3 * avg_color[0] - 0.3 * avg_color[1]
driver.setCruisingSpeed(speed)
driver.setSteeringAngle(angle)
diff --git a/src/Simulateur/controllers/controllerWorldInit/controllerWorldInit.py b/src/simulation/src/webots/controllers/controller_world_init/controller_world_init.py
similarity index 52%
rename from src/Simulateur/controllers/controllerWorldInit/controllerWorldInit.py
rename to src/simulation/src/webots/controllers/controller_world_init/controller_world_init.py
index d249ad4e..003e3adc 100644
--- a/src/Simulateur/controllers/controllerWorldInit/controllerWorldInit.py
+++ b/src/simulation/src/webots/controllers/controller_world_init/controller_world_init.py
@@ -1,27 +1,16 @@
-from typing import Tuple
-import numpy as np
from controller import Supervisor
-import os
-import sys
+from simulation import config as c
-# this is necessary because we are not in a package context
-# so we cannot do from ..config import *
-# SO EVEN IF IT LOOKS UGLY, WE HAVE TO DO THIS
-script_dir = os.path.dirname(os.path.abspath(__file__))
-controllers_path = os.path.join(script_dir, '../..')
-sys.path.append(controllers_path)
-from config import *
-
-
-
-def create_nodes(supervisor: Supervisor, n_vehicles: int):
+def create_nodes():
"""
Creates n_vehicles vehicles in the simulation
for each vehicle, create an emitter and a receiver in the supervisor
"""
+ supervisor = Supervisor()
+
root = supervisor.getRoot()
root_children_field = root.getField("children")
@@ -29,41 +18,39 @@ def create_nodes(supervisor: Supervisor, n_vehicles: int):
DEF WorldSupervisor Robot {{
supervisor TRUE
name "WorldSupervisor"
- controller "controllerWorldSupervisor"
+ controller "controller_world_supervisor"
children [
- {"\n".join([f' Emitter {{name "supervisor_emitter_{i}"}}' for i in range(n_vehicles)])}
- {"\n".join([f' Receiver {{name "supervisor_receiver_{i}"}}' for i in range(n_vehicles)])}
+ {"\n".join([f' Emitter {{name "supervisor_emitter_{i}"}}' for i in range(c.n_vehicles)])}
+ {"\n".join([f' Receiver {{name "supervisor_receiver_{i}"}}' for i in range(c.n_vehicles)])}
]
}}
"""
root_children_field.importMFNodeFromString(-1, proto_string)
- for i in range(n_vehicles):
+ for i in range(c.n_vehicles):
proto_string = f"""
DEF TT02_{i} TT02_2023b {{
name "TT02_{i}"
- controller "controllerVehicleDriver"
+ controller "controller_vehicle_driver"
color 0.0 0.0 1.0
- lidar_horizontal_resolution {lidar_horizontal_resolution}
- camera_horizontal_resolution {camera_horizontal_resolution}
+ lidar_horizontal_resolution {c.lidar_horizontal_resolution}
+ camera_horizontal_resolution {c.camera_horizontal_resolution}
}}
"""
root_children_field.importMFNodeFromString(-1, proto_string)
-
- for i in range(n_vehicles, n_vehicles + n_stupid_vehicles):
+ for i in range(c.n_vehicles, c.n_vehicles + c.n_stupid_vehicles):
proto_string = f"""
DEF TT02_{i} TT02_2023b {{
name "TT02_{i}"
controller "controller_violet"
color 0.0 0.0 0.0
- lidar_horizontal_resolution {lidar_horizontal_resolution}
- camera_horizontal_resolution {camera_horizontal_resolution}
+ lidar_horizontal_resolution {c.lidar_horizontal_resolution}
+ camera_horizontal_resolution {c.camera_horizontal_resolution}
}}
"""
root_children_field.importMFNodeFromString(-1, proto_string)
if __name__ == "__main__":
- S = Supervisor()
- create_nodes(S, n_vehicles)
+ create_nodes()
diff --git a/src/simulation/src/webots/controllers/controller_world_supervisor/__init__.py b/src/simulation/src/webots/controllers/controller_world_supervisor/__init__.py
new file mode 100644
index 00000000..933f966e
--- /dev/null
+++ b/src/simulation/src/webots/controllers/controller_world_supervisor/__init__.py
@@ -0,0 +1,4 @@
+from .checkpoint import Checkpoint
+from .checkpoint_manager import CheckpointManager, checkpoints
+
+__all__ = ["Checkpoint", "CheckpointManager", "checkpoints"]
diff --git a/src/Simulateur/controllers/controllerWorldSupervisor/checkpoint.py b/src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint.py
similarity index 84%
rename from src/Simulateur/controllers/controllerWorldSupervisor/checkpoint.py
rename to src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint.py
index 732925fa..21232e32 100644
--- a/src/Simulateur/controllers/controllerWorldSupervisor/checkpoint.py
+++ b/src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint.py
@@ -1,5 +1,9 @@
+from logging import DEBUG
+
import numpy as np
+from simulation import config as c
+
class Checkpoint:
def __init__(self, theta, x0, y0):
@@ -17,10 +21,11 @@ def create_vector_2d(self, supervisor):
length = 0.5
# debug flag to generate the cylinder and cone shapes
- DEBUG = False
- if DEBUG:
+ if c.LOG_LEVEL <= DEBUG:
# Cylinder (shaft)
- supervisor.getRoot().getField("children").importMFNodeFromString(-1, f"""
+ supervisor.getRoot().getField("children").importMFNodeFromString(
+ -1,
+ f"""
Transform {{
translation {self.x0} {self.y0} 0
rotation 0 1 0 {-np.pi / 2}
@@ -43,12 +48,15 @@ def create_vector_2d(self, supervisor):
}}
]
}}
- """)
+ """,
+ )
# Cone (arrowhead)
- supervisor.getRoot().getField("children").importMFNodeFromString(-1, f"""
+ supervisor.getRoot().getField("children").importMFNodeFromString(
+ -1,
+ f"""
Transform {{
- translation {self.x0 + np.cos(self.theta)*length/2} {self.y0 + np.sin(self.theta)*length/2} 0
+ translation {self.x0 + np.cos(self.theta) * length / 2} {self.y0 + np.sin(self.theta) * length / 2} 0
rotation 0 1 0 {-np.pi / 2}
children [
Transform {{
@@ -69,7 +77,8 @@ def create_vector_2d(self, supervisor):
}}
]
}}
- """)
+ """,
+ )
def check_plane(self, x, y):
"""
@@ -79,4 +88,6 @@ def check_plane(self, x, y):
which is equivalent to checking
u . (x, y) >= 0 but with an offset of (x0, y0) where u is a vector orthogonal to the hyperplane
"""
- return np.cos(self.theta)*(x - self.x0) + np.sin(self.theta)*(y - self.y0) >= 0
+ return (
+ np.cos(self.theta) * (x - self.x0) + np.sin(self.theta) * (y - self.y0) >= 0
+ )
diff --git a/src/Simulateur/controllers/controllerWorldSupervisor/checkpointmanager.py b/src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint_manager.py
similarity index 95%
rename from src/Simulateur/controllers/controllerWorldSupervisor/checkpointmanager.py
rename to src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint_manager.py
index 350091a6..c5d8e25d 100644
--- a/src/Simulateur/controllers/controllerWorldSupervisor/checkpointmanager.py
+++ b/src/simulation/src/webots/controllers/controller_world_supervisor/checkpoint_manager.py
@@ -1,13 +1,21 @@
+import os
from typing import List
-from checkpoint import Checkpoint
+
import numpy as np
from controller import Supervisor
-import os
+
+from . import Checkpoint
np.random.seed(os.getpid())
+
class CheckpointManager:
- def __init__(self, supervisor: Supervisor, checkpoints: List[Checkpoint], next_checkpoint=None):
+ def __init__(
+ self,
+ supervisor: Supervisor,
+ checkpoints: List[Checkpoint],
+ next_checkpoint=None,
+ ):
self.supervisor = supervisor
self.checkpoints = checkpoints
self.reset(next_checkpoint)
@@ -20,7 +28,6 @@ def update(self, x=None, y=None):
"""
# if x is None then y is None because of default value rules in python
-
if x is None or self.checkpoints[self.next_checkpoint].check_plane(x, y):
self.next_checkpoint = (self.next_checkpoint + 1) % len(self.checkpoints)
return True
@@ -53,7 +60,7 @@ def reset(self, i=None):
checkpoints = [
- [ # piste0.wbt
+ [ # piste0.wbt
Checkpoint(0, -0.314494, -2.47211),
Checkpoint(0, 1.11162, -2.56708),
Checkpoint(0.8, 2.54552, -2.27446),
@@ -86,7 +93,7 @@ def reset(self, i=None):
Checkpoint(-0.3, -2.89371, -2.49154),
Checkpoint(0, -2.01029, -2.51669),
],
- [ # piste1.wbt
+ [ # piste1.wbt
Checkpoint(1.57, 5.52, -2.25),
Checkpoint(1.57, 5.52, -1.25),
Checkpoint(1.57, 5.52, -0.25),
@@ -144,9 +151,7 @@ def reset(self, i=None):
Checkpoint(1.05, 5.43, -5.00),
Checkpoint(1.57, 5.52, -3.25),
Checkpoint(1.57, 5.52, -4.25),
-
],
- [ # piste2.wbt
-
- ]
+ [ # piste2.wbt
+ ],
]
diff --git a/src/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py b/src/simulation/src/webots/controllers/controller_world_supervisor/controller_world_supervisor.py
similarity index 51%
rename from src/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py
rename to src/simulation/src/webots/controllers/controller_world_supervisor/controller_world_supervisor.py
index b0639ce3..5adff592 100644
--- a/src/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py
+++ b/src/simulation/src/webots/controllers/controller_world_supervisor/controller_world_supervisor.py
@@ -1,26 +1,18 @@
-import re
-from typing import *
-import numpy as np
-import gymnasium as gym
-
-from checkpointmanager import CheckpointManager, checkpoints
+import logging
+import numpy as np
from controller import Supervisor
-supervisor = Supervisor()
-import torch.nn as nn
-import psutil
-import os
-import sys
+# I cannot use relative imports because the file is directly launched by webots
+from webots.controllers.controller_world_supervisor import (
+ CheckpointManager,
+ checkpoints,
+)
-# this is necessary because we are not in a package context
-# so we cannot do from ..config import *
-# SO EVEN IF IT LOOKS UGLY, WE HAVE TO DO THIS
-script_dir = os.path.dirname(os.path.abspath(__file__))
-controllers_path = os.path.join(script_dir, '../..')
-sys.path.append(controllers_path)
+from simulation import config as c
+from utils.simulation_rank import get_simulation_rank
-from config import *
+supervisor = Supervisor()
checkpoint_positions = [
[-0.314494, -2.47211, 0.0391],
@@ -56,25 +48,7 @@
[-2.01029, -2.51669, 0.0391],
]
-# webots precess launches webots-bin internally who is the process that launches the controllers
-# that's why we need to get the pppid
-proc = psutil.Process(os.getpid()) #current
-parent = proc.parent() #parent
-grandparent = parent.parent() if parent else None #grandparent
-pppid = str(grandparent.pid)
-
-
-simulation_rank = int(
- re.search(
- pppid + r" (\d+)",
- open("/tmp/autotech/simulationranks", "r").read(),
- re.MULTILINE
- ).group(1)
-)
-
-
-print(f"CLIENT ?{pppid}? {simulation_rank=}")
-
+simulation_rank = get_simulation_rank()
class WebotsVehicleManager:
@@ -87,58 +61,68 @@ class WebotsVehicleManager:
def __init__(self, vehicle_rank: int):
self.vehicle_rank = vehicle_rank
- self.checkpoint_manager = CheckpointManager(supervisor, checkpoints[simulation_rank % n_map], vehicle_rank)
+ self.checkpoint_manager = CheckpointManager(
+ supervisor, checkpoints[simulation_rank % c.n_map], vehicle_rank
+ )
- self.v_min = 1 #np.random.rand()*2 + 0.5 # 0.5 to 2.5
- self.v_max = 9 #np.random.rand()*5 + 4 # 5 to 10
+ self.v_min = 1
+ self.v_max = 9
basicTimeStep = int(supervisor.getBasicTimeStep())
self.sensorTime = basicTimeStep // 4
# negative value so that the first reset is not skipped
self.last_reset = -1e6
- proc = psutil.Process(os.getpid()) #current
- parent = proc.parent() #parent
- grandparent = parent.parent() if parent else None #grandparent
- pppid = str(grandparent.pid)
-
+ self.simulation_rank = get_simulation_rank()
- self.simulation_rank = int(
- re.search(
- pppid + r" (\d+)",
- open("/tmp/autotech/simulationranks", "r").read(),
- re.MULTILINE
- ).group(1)
+ self.handler = logging.FileHandler(
+ f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.vehicle_rank}.log"
)
-
- self.handler = logging.FileHandler(f"/tmp/autotech/Voiture_{self.simulation_rank}_{self.vehicle_rank}.log")
- self.handler.setFormatter(FORMATTER)
- self.log = logging.getLogger("SUPERVISOR")
- self.log.setLevel(level=LOG_LEVEL)
+ self.handler.setFormatter(c.FORMATTER)
+ self.log = logging.getLogger(
+ f"SUPERVISOR_{self.simulation_rank}_{self.vehicle_rank}"
+ )
+ self.log.setLevel(level=c.LOG_LEVEL)
self.log.addHandler(self.handler)
-
self.log.debug("Connection to the vehicle")
- self.fifo_r = open(f"/tmp/autotech/{self.simulation_rank}_{self.vehicle_rank}tosupervisor.pipe", "rb")
+ self.fifo_r = open(
+ f"/tmp/autotech/{self.simulation_rank}_{self.vehicle_rank}tosupervisor.pipe",
+ "rb",
+ )
self.log.debug("Connection to the server")
- self.fifo_w = open(f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe", "wb")
-
+ self.fifo_w = open(
+ f"/tmp/autotech/{simulation_rank}_{vehicle_rank}toserver.pipe", "wb"
+ )
- self.translation_field = supervisor.getFromDef(f"TT02_{self.vehicle_rank}").getField("translation") # may cause access issues ...
- self.rotation_field = supervisor.getFromDef(f"TT02_{self.vehicle_rank}").getField("rotation") # may cause access issues ...
+ self.translation_field = supervisor.getFromDef(
+ f"TT02_{self.vehicle_rank}"
+ ).getField("translation") # may cause access issues ...
+ self.rotation_field = supervisor.getFromDef(
+ f"TT02_{self.vehicle_rank}"
+ ).getField("rotation") # may cause access issues ...
# returns the lidar data of all vehicles
def observe(self):
# gets from Vehicle
self.log.debug("trying to observe")
- obs = np.frombuffer(self.fifo_r.read(np.dtype(np.float32).itemsize * (n_sensors + lidar_horizontal_resolution + camera_horizontal_resolution)), dtype=np.float32)
- self.log.info(f"observing {obs=}")
+ obs = np.frombuffer(
+ self.fifo_r.read(
+ np.dtype(np.float32).itemsize
+ * (
+ c.n_sensors
+ + c.lidar_horizontal_resolution
+ + c.camera_horizontal_resolution
+ )
+ ),
+ dtype=np.float32,
+ )
+ self.log.debug(f"observing {obs=}")
return obs
-
# reset the gym environment reset
def reset(self, seed=None):
- self.log.debug("trying to reset vehicle")
+ self.log.info("reseting vehicle")
# this has to be done otherwise thec cars will shiver for a while sometimes when respawning and idk why
if supervisor.getTime() - self.last_reset >= 1e-1:
self.log.debug("getting info from vehicle")
@@ -156,36 +140,39 @@ def reset(self, seed=None):
vehicle.resetPhysics()
self.log.info("vehicle reset done")
-
- obs = np.zeros(n_sensors + lidar_horizontal_resolution + camera_horizontal_resolution, dtype=np.float32)
+ obs = np.zeros(
+ c.n_sensors
+ + c.lidar_horizontal_resolution
+ + c.camera_horizontal_resolution,
+ dtype=np.float32,
+ )
info = {}
return obs, info
# step function of the gym environment
def step(self):
- #action_steering = np.linspace(-.4, .4, n_actions_steering, dtype=np.float32)[action[0], None]
- #action_speed = np.linspace(self.v_min, self.v_max, n_actions_speed, dtype=np.float32)[action[1], None]
-
# we should add a beacon sensor pointing upwards to detect the beacon
self.log.debug("getting observation")
obs = self.observe()
self.log.info(f"observed {obs=}")
- sensor_data = obs[:n_sensors]
+ sensor_data = obs[: c.n_sensors]
reward = 0
done = np.False_
truncated = np.False_
x, y, z = self.translation_field.getSFVec3f()
b_past_checkpoint = self.checkpoint_manager.update(x, y)
- b_collided, = sensor_data # unpack sensor data
+ (b_collided,) = sensor_data # unpack sensor data
- if b_collided or (z < -10):
- #print(f"CLIENT{simulation_rank}/{self.vehicle_rank} : {b_collided=}, {z=}")
+ if z < -10:
reward = np.float32(0.0)
done = np.True_
+ elif b_collided:
+ reward = np.float32(-0.5)
+ done = np.False_
elif b_past_checkpoint:
- reward = np.float32(1.0) #* np.cos(self.checkpoint_manager.getAngle() - self.rotation_field.getSFRotation()[3], dtype=np.float32)
+ reward = np.float32(1.0)
done = np.False_
else:
reward = np.float32(0.05)
@@ -195,36 +182,27 @@ def step(self):
def main():
-
- envs = [WebotsVehicleManager(i) for i in range(n_vehicles)]
- print("CLIENT ALL : envs created")
- # check_env(env)
-
- logdir = "./Webots_tb/"
+ envs = [WebotsVehicleManager(i) for i in range(c.n_vehicles)]
supervisor.step()
-
+
for i, e in enumerate(envs):
- e.log.debug("CLIENT : reset")
e.reset(i)
- for i in range(n_vehicles, n_vehicles + n_stupid_vehicles):
+ for i in range(c.n_vehicles, c.n_vehicles + c.n_stupid_vehicles):
(
- supervisor
- .getFromDef(f"TT02_{i}")
+ supervisor.getFromDef(f"TT02_{i}")
.getField("translation")
.setSFVec3f(checkpoint_positions[i])
)
- last_moved = [0 for _ in range(n_stupid_vehicles)]
+ last_moved = np.zeros(c.n_stupid_vehicles)
while supervisor.step() != -1:
- #print("CLIENT ALL : begin step")
- #Prédiction pour séléctionner une action à partir de l"observation
for e in envs:
obs, reward, done, truncated, info = e.step()
- if done:
- obs, info = e.reset()
+ if done:
+ obs, info = e.reset()
e.log.info(f"sending {obs=}")
e.fifo_w.write(obs.tobytes())
@@ -234,25 +212,25 @@ def main():
e.fifo_w.write(done.tobytes())
e.log.info(f"sending {truncated=}")
e.fifo_w.write(truncated.tobytes())
- e.fifo_w.flush()
+ e.fifo_w.flush()
-
- for i in range(n_stupid_vehicles):
- tr_field = supervisor.getFromDef(f"TT02_{n_vehicles + i}").getField("translation")
+ for i in range(c.n_stupid_vehicles):
+ tr_field = supervisor.getFromDef(f"TT02_{c.n_vehicles + i}").getField(
+ "translation"
+ )
speed = np.linalg.norm(tr_field.getSFVec3f())
if speed >= 0.1:
last_moved[i] = supervisor.getTime()
else:
- print("DIDNT MOVE")
+ envs[i].log.debug("did not move")
if supervisor.getTime() - last_moved[i]:
- print("DIDNT MOVE FOR 1s")
+ envs[i].log.info(
+ "resetting position because did not move for more than 1s"
+ )
tr_field.setSFVec3f(checkpoint_positions[0])
-
-
if __name__ == "__main__":
- print("BEGINS MAIN FROM CONTROLLER WORLD SUPERVISOR", flush=True)
main()
diff --git a/src/Simulateur/protos/.~lock.TT02.proto# b/src/simulation/src/webots/protos/.~lock.TT02.proto#
similarity index 100%
rename from src/Simulateur/protos/.~lock.TT02.proto#
rename to src/simulation/src/webots/protos/.~lock.TT02.proto#
diff --git a/src/Simulateur/protos/Carrosserie_Audi_Light.stl b/src/simulation/src/webots/protos/Carrosserie_Audi_Light.stl
similarity index 100%
rename from src/Simulateur/protos/Carrosserie_Audi_Light.stl
rename to src/simulation/src/webots/protos/Carrosserie_Audi_Light.stl
diff --git a/src/Simulateur/protos/Carrosserie_BMW_light.stl b/src/simulation/src/webots/protos/Carrosserie_BMW_light.stl
similarity index 100%
rename from src/Simulateur/protos/Carrosserie_BMW_light.stl
rename to src/simulation/src/webots/protos/Carrosserie_BMW_light.stl
diff --git a/src/Simulateur/protos/ChevroletCamaroLight.stl b/src/simulation/src/webots/protos/ChevroletCamaroLight.stl
similarity index 100%
rename from src/Simulateur/protos/ChevroletCamaroLight.stl
rename to src/simulation/src/webots/protos/ChevroletCamaroLight.stl
diff --git a/src/Simulateur/protos/Hokuyo.proto b/src/simulation/src/webots/protos/Hokuyo.proto
similarity index 100%
rename from src/Simulateur/protos/Hokuyo.proto
rename to src/simulation/src/webots/protos/Hokuyo.proto
diff --git a/src/Simulateur/protos/TT02Wheel.proto b/src/simulation/src/webots/protos/TT02Wheel.proto
similarity index 100%
rename from src/Simulateur/protos/TT02Wheel.proto
rename to src/simulation/src/webots/protos/TT02Wheel.proto
diff --git a/src/Simulateur/protos/TT02_2023b.proto b/src/simulation/src/webots/protos/TT02_2023b.proto
similarity index 100%
rename from src/Simulateur/protos/TT02_2023b.proto
rename to src/simulation/src/webots/protos/TT02_2023b.proto
diff --git a/src/Simulateur/protos/icons/Altino.png b/src/simulation/src/webots/protos/icons/Altino.png
similarity index 100%
rename from src/Simulateur/protos/icons/Altino.png
rename to src/simulation/src/webots/protos/icons/Altino.png
diff --git a/src/Simulateur/protos/icons/Car.png b/src/simulation/src/webots/protos/icons/Car.png
similarity index 100%
rename from src/Simulateur/protos/icons/Car.png
rename to src/simulation/src/webots/protos/icons/Car.png
diff --git a/src/Simulateur/protos/icons/RpLidarA2.png b/src/simulation/src/webots/protos/icons/RpLidarA2.png
similarity index 100%
rename from src/Simulateur/protos/icons/RpLidarA2.png
rename to src/simulation/src/webots/protos/icons/RpLidarA2.png
diff --git a/src/Simulateur/worlds/ImageToStl.com_virage.obj b/src/simulation/src/webots/worlds/ImageToStl.com_virage.obj
similarity index 100%
rename from src/Simulateur/worlds/ImageToStl.com_virage.obj
rename to src/simulation/src/webots/worlds/ImageToStl.com_virage.obj
diff --git a/src/Simulateur/worlds/[DEPRECATED]piste.wbt b/src/simulation/src/webots/worlds/[DEPRECATED]piste.wbt
similarity index 99%
rename from src/Simulateur/worlds/[DEPRECATED]piste.wbt
rename to src/simulation/src/webots/worlds/[DEPRECATED]piste.wbt
index a3545c45..dc8c4b5d 100644
--- a/src/Simulateur/worlds/[DEPRECATED]piste.wbt
+++ b/src/simulation/src/webots/worlds/[DEPRECATED]piste.wbt
@@ -235,8 +235,6 @@ Wall {
DEF WorldSupervisor Robot {
supervisor TRUE
name "WorldSupervisor"
- controller "controllerWorldSupervisor"
+ controller "controller_world_supervisor"
children []
}
-
-
diff --git a/src/Simulateur/worlds/piste0.wbt b/src/simulation/src/webots/worlds/piste0.wbt
similarity index 99%
rename from src/Simulateur/worlds/piste0.wbt
rename to src/simulation/src/webots/worlds/piste0.wbt
index fa3bd602..e3a4e638 100644
--- a/src/Simulateur/worlds/piste0.wbt
+++ b/src/simulation/src/webots/worlds/piste0.wbt
@@ -2230,7 +2230,7 @@ Solid {
DEF WorldInit Robot {
supervisor TRUE
name "WorldInit"
- controller "controllerWorldInit"
+ controller "controller_world_init"
children [
]
}
diff --git a/src/Simulateur/worlds/piste1.wbt b/src/simulation/src/webots/worlds/piste1.wbt
similarity index 99%
rename from src/Simulateur/worlds/piste1.wbt
rename to src/simulation/src/webots/worlds/piste1.wbt
index 4f320a44..30782538 100644
--- a/src/Simulateur/worlds/piste1.wbt
+++ b/src/simulation/src/webots/worlds/piste1.wbt
@@ -3166,7 +3166,7 @@ Solid {
DEF WorldInit Robot {
supervisor TRUE
name "WorldInit"
- controller "controllerWorldInit"
+ controller "controller_world_init"
children [
]
}
diff --git a/src/Simulateur/worlds/piste2.wbt b/src/simulation/src/webots/worlds/piste2.wbt
similarity index 100%
rename from src/Simulateur/worlds/piste2.wbt
rename to src/simulation/src/webots/worlds/piste2.wbt
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-
-
-Les Lidar utiliser par INTech sont des [UST-10LX](https://www.hokuyo-aut.jp/search/single.php?serial=167) concu et vendu par Hokuyo. Une documentation non officel (mais plutot complete [existe ici])(https://sourceforge.net/p/urgnetwork/wiki/Home/). Le manuel
-du l'UST-10lx est [télécharchable ici](./Instruction_manual_UST-10LX_MRS0020D_en_1513910662-1.pdf)
-
-## UST-10lx connectivity
-
-Il communique par Ethernet en utilisant le protocole [Secure Communications Interoperability Protocol (SCIP)](https://en.wikipedia.org/wiki/Secure_Communications_Interoperability_Protocol). Ce protocol peut faire peur a premiere vue mais nous n'utilison que les commande decrit [ici](https://sourceforge.net/p/urgnetwork/wiki/scip_en/). Pour communiquer par ethernet, les lidar possede une adresse IP:
-
-```
-192.168.0.10:10940/24
-```
-
-Il faut donc choisir une adresse ip dans le bon sous-resaux. Nous avons arbitrairement choisi:
-
-```
-192.168.0.20
-```
-
-## UST-10lx specs
-
-
-
-
-| Spec | Value | Unit |
-|---------------------|----------------------|------|
-| Min distance | 20 | mm |
-| Max distance | 30000 | mm |
-| Accuracy | ±40 | mm |
-| Scan angle | 270 | ° |
-| Angular resolution | 0.25 | pt/° |
-| Angular resolution | 1440 | pt/tr|
-| Scan rate | 40 | Hz |
-| Poids | 130 | g |
-| Voltage | 10-30 | VDC |
-| Current @24VDC | 150 (450 at startup) | mA |
-
-
-## Using HokuyoReader class
-
-This class is from [cassc/tcp_hokuyo.py](https://gist.github.com/cassc/26ac479624cb028b2567491a68c34fb8)
-This class is simpler than [hokuyolx](#using-hokuyolx-class)
-
-Create the class instance with
-``` python
-sensor = HokuyoReader(IP, PORT)
-```
-
-use `HokuyoReader.stop()` to get rid of any leftover data or problems from an improper shutdown and start mesuring with `HokuyoReader.startContinuous(0, 1080)` with 0 and 1080 the steps that are mesured
-
-```pyhton
-sensor.stop()
-sensor.startContinuous(0, 1080)
-```
-
-Distances can be retrived as a numpy array with the `HokuyoReader.rDistance()` (r standing for radial)
-
-```python
-distance_array=sensor.rDistance()
-```
-
-Use `HokuyoReader.stop()` to gracefully shutdown the lidar
-
-```pyhton
-sensor.stop()
-
-```
-
-!!! note
- This class has been renamed to Lidar to be more descriptive in code.
-
-!!! note
- After contacting the author, he has agreed to licence his script as MIT (none before), allowing us to do the same on this repository.
-
-## Using Hokuyolx class
-
-This class comes from [SkoltechRobotics/hokuyolx](https://github.com/SkoltechRobotics/hokuyolx).
-This class has considerably more options than [HokuyoReader](#using-hokuyoreader-class) but is more complicated to understand. This class is documented at [http://hokuyolx.rtfd.org/](http://hokuyolx.rtfd.org/). As of 12/12/24 it doesn't work out off the box and we use [HokuyoReader](#using-hokuyoreader-class)
-
-## Les galères
-
-On a eu pas mal de galères avec le lidar qui ce déconnecter de manière intempestive. En temps normal, soit le lidar est débranché, l’interface eth0 est Down et elle n’a pas d’IP, soit le lidar est branché et l’interface est UP avec un IP static (192.168.1.20). Malheureusement, des fois, pour des raisons un peu mystère, l’interface rester UP, mais perd son IP fixe.
-
-Débrancher et rebrancher le lidar a systématiquement marché, mais n’adresse pas la cause du problème. 2 théories :
-- Le câble est baisé et fait un faux contact
-- Le Network manager de la RPI fout la merde
-
-Comme la languette de rétention, Ethernet était un peu mort donc on a reserti un nouveau connecteur (Merci MiNET ❤️). Cela a résolu une grande partie du problème et nous n’avons observer que deux récidives : l’une directement après l’autre le 03/04/25 lors des essais sur piste.
-
-###Quelques commandes utiles
-
-```bash
-ip a
-```
-Permets de voir les différentes interfaces réseau, leurs statuts et les IP associer.
-
-```bash
-watch -n 0.1 "ip a"
-```
-Permet d’exécuter toutes les 0,1 s (le max) une commande et d’affiche le résultat, ici`ip a`.
-
diff --git a/docs/Simulateur.md b/docs/Simulateur.md
deleted file mode 100644
index e568c8d5..00000000
--- a/docs/Simulateur.md
+++ /dev/null
@@ -1,132 +0,0 @@
-# Simulateur
-
-Cette page de documentation détaille le fonctionnement des scripts utilisés dans le cadre de l'utilisation de [Webots](https://cyberbotics.com) pour entrainer une IA sur une simulation de piste.
-
----
-## Objectifs
-
-On cherche à simuler le déplacement d'une voiture dans un environnement semblable à la piste de la course. A chaque étape de la simulation on donne en entrée les données des différents capteurs à un model qui nous renvoie une consigne d'angle de roues à appliquer au véhicule pour la prochaine étape de la simulation.
-
-
----
-## Organisation basique à une voiture
-
-Une première approche pourrairt être d'organiser notre monde avec une [Piste](#piste) et une [Voiture](#voiture). Dans cette approche toute la logique serait géré au niveau du [Driver](https://cyberbotics.com/doc/automobile/driver-library) de la [Voiture](#voiture):
-- obtenir la data des capteurs
-- exécuter le model pour obtenir une consigne
-- update l'état de la voiture en accord avec cette consigne
-- puis finalement calculer les rewards données à la fin de chaque episode pour entrainer l'[IA](./IA.md)
-
-!!! note
- Le problème de cette architecture est qu'elle n'est pas scalable. Elle nous force à n'utiliser qu'une seule voiture en même temps car en augmentant le nombre de voitures, elles gèreraient toutes leur propre model en local et on entraînerait donc _n_ models au lieu d'un 1 ce qui est inutile.
-
-
-```
-
-