From fb6e21b1cab9646fcf6ec4acc1f080bc2ac6a421 Mon Sep 17 00:00:00 2001 From: matthiasbienvenu Date: Thu, 21 Nov 2024 14:49:03 +0100 Subject: [PATCH 1/9] Remove obsolete files, change file/directory names and create a car proto --- .../Controller_voiture/Controller_voiture.py | 266 -- .../V2 et V3/Controller_voiture_v2.py | 210 -- .../V2 et V3/Controller_voiture_v3.py | 184 -- .../Voiture_autonome_Webots_A2C.zip | Bin 134602 -> 0 bytes .../controller_Gym_v2.py | 213 -- .../controller_Jessica/controller_Jessica.py | 196 -- Simulateur/Readme.md | 19 - .../SimulateurMatlabCentraleSupelec/README.md | 1 - .../ImageToStl.com_bloc_0.5m.obj | 822 ------ .../ImageToStl.com_bloc_1m.obj | 822 ------ .../ImageToStl.com_virage.obj | 1662 ------------ Simulateur/SimulateurWebotsENS2022/Piste3.wbt | 886 ------ .../Simulateur_CoVAPSy_Webots2023b_Base.zip | Bin 1087763 -> 0 bytes .../__pycache__/car_logic.cpython-38.pyc | Bin 10356 -> 0 bytes .../controllers/car_logic.py | 311 --- .../minimalController/minimalController.py | 65 - .../__pycache__/trajectory.cpython-310.pyc | Bin 517 -> 0 bytes .../__pycache__/trajectory.cpython-38.pyc | Bin 471 -> 0 bytes .../utils/__pycache__/utils.cpython-310.pyc | Bin 873 -> 0 bytes .../utils/__pycache__/utils.cpython-38.pyc | Bin 848 -> 0 bytes .../controllers/utils/trajectory.py | 12 - .../controllers/utils/utils.py | 18 - .../Webots-Efrei-2023/worlds/.Circuit.wbproj | 9 - .../Webots-Efrei-2023/worlds/Circuit.wbt | 2404 ----------------- .../trajectories/Circuit-trajectory.json | 1 - ...tfevents.1664312862.Chrysanthe-TOSH.2080.0 | Bin ...tfevents.1664356896.Chrysanthe-TOSH.6960.0 | Bin ...tfevents.1664442064.Chrysanthe-TOSH.5336.0 | Bin ...tfevents.1664793148.Chrysanthe-TOSH.6404.0 | Bin ...tfevents.1664793499.Chrysanthe-TOSH.6312.0 | Bin .../controllerVehiculeDriver.py} | 194 +- .../controllerWorldInitSupervisor.py | 0 Simulateur/protos/car.proto | 894 ++++++ .../worlds/.Piste.wbproj | 6 +- .../worlds/.empty.wbproj | 0 .../worlds/Piste.wbt => worlds/car.wbo} | 224 -- Simulateur/worlds/piste.wbt | 224 ++ 37 files changed, 1216 insertions(+), 8427 deletions(-) delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/Controller_voiture/Controller_voiture.py delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/V2 et V3/Controller_voiture_v2.py delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/V2 et V3/Controller_voiture_v3.py delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Voiture_autonome_Webots_A2C.zip delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/controller_Gym_v2.py delete mode 100644 Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Jessica/controller_Jessica.py delete mode 100644 Simulateur/Readme.md delete mode 100644 Simulateur/SimulateurMatlabCentraleSupelec/README.md delete mode 100644 Simulateur/SimulateurWebotsENS2022/ImageToStl.com_bloc_0.5m.obj delete mode 100644 Simulateur/SimulateurWebotsENS2022/ImageToStl.com_bloc_1m.obj delete mode 100644 Simulateur/SimulateurWebotsENS2022/ImageToStl.com_virage.obj delete mode 100644 Simulateur/SimulateurWebotsENS2022/Piste3.wbt delete mode 100644 Simulateur/Simulateur_CoVAPSy_Webots2023b_Base.zip delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/__pycache__/car_logic.cpython-38.pyc delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/car_logic.py delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/minimalController/minimalController.py delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-310.pyc delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-38.pyc delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-310.pyc delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-38.pyc delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/trajectory.py delete mode 100644 Simulateur/Webots-Efrei-2023/controllers/utils/utils.py delete mode 100644 Simulateur/Webots-Efrei-2023/worlds/.Circuit.wbproj delete mode 100644 Simulateur/Webots-Efrei-2023/worlds/Circuit.wbt delete mode 100644 Simulateur/Webots-Efrei-2023/worlds/trajectories/Circuit-trajectory.json rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011 => controllers/VehiculeDriver}/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011 => controllers/VehiculeDriver}/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011 => controllers/VehiculeDriver}/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011 => controllers/VehiculeDriver}/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011 => controllers/VehiculeDriver}/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/controller_Gym.py => controllers/VehiculeDriver/controllerVehiculeDriver.py} (52%) create mode 100644 Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py create mode 100644 Simulateur/protos/car.proto rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022 => }/worlds/.Piste.wbproj (52%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022 => }/worlds/.empty.wbproj (100%) rename Simulateur/{Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/Piste.wbt => worlds/car.wbo} (99%) create mode 100644 Simulateur/worlds/piste.wbt diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/Controller_voiture/Controller_voiture.py b/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/Controller_voiture/Controller_voiture.py deleted file mode 100644 index dfaec25..0000000 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/Controller_voiture/Controller_voiture.py +++ /dev/null @@ -1,266 +0,0 @@ -"""Voiture autonome avec utilisation -self.lidar avec WIBORS""" - -import numpy as np -import random -import gym -import numpy as np -from controller import Supervisor -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 - -#Programme principal -# while self.conduire.step() != -1: -# a = random.randint(1,10) -# rouler(a) -# reward = evaluer() -# #print(reward) -# etat = observe(self.lidar, 50) - #print(self.lidar.getNumberOfPoints()) - - -#Fonction d'initialisation -def initialisation(robot) : - INITIAL_trans = [-3, 4, 0.0195182] - INITIAL_rot=[0.000206508, -0.349092, 0.937089 , 0.00146275] - - #Paramètre de la voiture (position, etc..) - voiture_robot = robot.getFromDef("vehicle") - trans_champs = voiture_robot.getField("translation") - rot_champs = voiture_robot.getField("rotation") - - trans_champs.setSFVec3f(INITIAL_trans) - rot_champs.setSFRotation(INITIAL_rot) - -#--------------GYM---------------------------- - -#Création de l'environnement GYM -class WebotsGymEnvironment(Supervisor, gym.Env) : - def __init__(self, max_episode_steps=1000): - #super().__init__() - self.action_space = gym.spaces.Discrete(10) - min = np.array(4*[0.0, 0.0]) - max = np.array(4*[1000.0,1000.0]) - self.observation_space = gym.spaces.Box(min.flatten(), max.flatten(), dtype=np.float32) -# print(self.observation_space) - - self.spec = gym.envs.registration.EnvSpec(id='WebotsEnv-v0', max_episode_steps=max_episode_steps) - self.done = False - self.state = None - - self.conduire = Driver() - self.lidar = Lidar("lidar") - - - basicTimeStep = int(self.conduire.getBasicTimeStep()) - sensorTimeStep = 4 * basicTimeStep - - #Capteur self.lidar - self.lidar.enable(sensorTimeStep) - self.lidar.enablePointCloud() - - #Capteur de distance - self.capteur_avant = self.conduire.getDevice('front_center_sensor') - self.capteur_gauche = self.conduire.getDevice('side_left_sensor') - self.capteur_droite = self.conduire.getDevice('side_right_sensor') - self.capteur_avant.enable(sensorTimeStep) - self.capteur_gauche.enable(sensorTimeStep) - self.capteur_droite.enable(sensorTimeStep) - - #Capteur de balise - self.capteur_balise = self.conduire.getDevice('capteur_balise') - self.capteur_balise.enable(sensorTimeStep) - - - #Vérification de l'état de la voiture - #Retourner valeur [distance angle] -# def observe(lidar_sensor,lidar_nb_pts) : -# pt=np.array(lidar_sensor.getRangeImage()) -# pt=pt.reshape((pt.shape[0],1))*1000 -# a=np.linspace(0,360, lidar_nb_pts) -# a=a.reshape((a.shape[0],1)) -# datas=np.hstack((pt,a)) -# etat = np.array(datas).astype(np.float32) -# etat_1D = etat.flatten() -# #for i in a : -# #if i == 'inf' : -# #a[i] = 0 -# #print(np.shape(a)) -# return etat_1D - -# #Fonction pour detection de collision -# def evaluer(): -# recompense = 0 -# avant = capteur_avant.getValue() -# gauche = capteur_gauche.getValue() -# droite = capteur_droite.getValue() -# balise = capteur_balise.getValue() -# -# if avant >= 900 : -# print("Collision avant") -# initialisation(self.conduire) #Réinitialisation de position en cas de crash -# recompense = -10 -# done = True -# elif (avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937 : -# print("Collision gauche") -# initialisation(self.conduire) -# recompense = -10 -# done = True -# elif (avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961 : -# print("Collision droite") -# initialisation(self.conduire) -# recompense = -10 -# done = True -# elif balise > 700 : -# done = False -# print("Balise passée") -# recompense = 10 -# else : -# done = False -# recompense = 0 -# -# return recompense - -# #Fonction épisode -# def is_done() : -# if not done : -# return False -# else : -# return True -# - #Affichage - def view() : - pass - - def reset(self): - initialisation(self.conduire) - #obs = observe(self.lidar,self.lidar.getNumberOfPoints()) - pt=np.array(self.lidar.getRangeImage()) - pt=pt.reshape((pt.shape[0],1))*1000 - a=np.linspace(0,360, self.lidar.getNumberOfPoints()) - a=a.reshape((a.shape[0],1)) - datas=np.hstack((pt,a)) - etat = np.array(datas).astype(np.float32) - obs = etat.flatten() - #for i in a : - #if i == 'inf' : - #a[i] = 0 - #print(np.shape(a)) -# print(np.shape(obs)) - return obs - - def step(self, action): - #Etape - if action == 0 : - self.conduire.setSteeringAngle(-0.4) - self.conduire.setCruisingSpeed(0.8) - elif action == 1 : - self.conduire.setSteeringAngle(-0.1) - self.conduire.setCruisingSpeed(0.8) - elif action == 2 : - self.conduire.setSteeringAngle(0) - self.conduire.setCruisingSpeed(0.8) - elif action == 3 : - self.conduire.setSteeringAngle(0.1) - self.conduire.setCruisingSpeed(0.8) - elif action == 4 : - self.conduire.setSteeringAngle(0.4) - self.conduire.setCruisingSpeed(0.8) - elif action == 5 : - self.conduire.setSteeringAngle(-0.4) - self.conduire.setCruisingSpeed(1.8) - elif action == 6 : - self.conduire.setSteeringAngle(-0.1) - self.conduire.setCruisingSpeed(1.8) - elif action == 7 : - self.conduire.setSteeringAngle(0) - self.conduire.setCruisingSpeed(1.8) - elif action == 8 : - self.conduire.setSteeringAngle(0.1) - self.conduire.setCruisingSpeed(1.8) - elif action == 9 : - self.conduire.setSteeringAngle(0.4) - self.conduire.setCruisingSpeed(1.8) -# obs = observe(self.lidar, self.lidar.getNumberOfPoints()) - - #Observation - pt=np.array(self.lidar.getRangeImage()) - pt=pt.reshape((pt.shape[0],1))*1000 - a=np.linspace(0,360, self.lidar.getNumberOfPoints()) - a=a.reshape((a.shape[0],1)) - datas=np.hstack((pt,a)) - etat = np.array(datas).astype(np.float32) - obs = etat.flatten() - #for i in a : - #if i == 'inf' : - #a[i] = 0 - #print(np.shape(a)) - - #Recompense - recompense = 0 - avant = self.capteur_avant.getValue() - gauche = self.capteur_gauche.getValue() - droite = self.capteur_droite.getValue() - balise = self.capteur_balise.getValue() - - if avant >= 900 : - print("Collision avant") - initialisation(self.conduire) #Réinitialisation de position en cas de crash - recompense = -10 - done = True - elif (avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937 : - print("Collision gauche") - initialisation(self.conduire) - recompense = -10 - done = True - elif (avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961 : - print("Collision droite") - initialisation(self.conduire) - recompense = -10 - done = True - elif balise > 700 : - done = False - print("Balise passée") - recompense = 10 - else : - done = False - recompense = 0 - - reward = recompense - - #is done - if not done : - done = False - else : - done = True - - return obs, reward, done, {} - - def render(self, mode="human", close=False): - view() - - -def main() : - env = WebotsGymEnvironment() - check_env(env) - -# # Entrainnement - model = PPO('MlpPolicy', env, n_steps=2048, verbose=1) - model.learn(total_timesteps=15) - - obs = env.reset() - - for _ in range(20): - #Prédiction pour séléctionner une action à partir de l'observation - action, _states = model.predict(obs, deterministic=True) - obs, reward, done, info = env.step(action) - #print(obs, reward, done, info) - if done: - obs = env.reset() - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/V2 et V3/Controller_voiture_v2.py b/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/V2 et V3/Controller_voiture_v2.py deleted file mode 100644 index 4300445..0000000 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/V2 et V3/Controller_voiture_v2.py +++ /dev/null @@ -1,210 +0,0 @@ -"""Voiture autonome avec utilisation -LIDAR avec WIBORS""" - -import numpy as np -import random -import gym -import numpy as np -from controller import Supervisor -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 - -# variable -cumul_recompense = 0 -done = False - -conduire = Driver() -lidar = Lidar("lidar") - -#Paramètre de la voiture (position, etc..) -voiture_robot = conduire.getFromDef("vehicle") -trans_champs = voiture_robot.getField("translation") -rot_champs = voiture_robot.getField("rotation") - - -basicTimeStep = int(conduire.getBasicTimeStep()) -sensorTimeStep = 4 * basicTimeStep - -#Capteur LIDAR -lidar.enable(sensorTimeStep) -lidar.enablePointCloud() - -#Capteur de distance -capteur_avant = conduire.getDevice('front_center_sensor') -capteur_gauche = conduire.getDevice('side_left_sensor') -capteur_droite = conduire.getDevice('side_right_sensor') -capteur_avant.enable(sensorTimeStep) -capteur_gauche.enable(sensorTimeStep) -capteur_droite.enable(sensorTimeStep) - -#Capteur de balise -capteur_balise = conduire.getDevice('capteur_balise') -capteur_balise.enable(sensorTimeStep) - -#Fonction d'initialisation -def initialisation() : - INITIAL_trans = [-3, 4, 0.0195182] - INITIAL_rot=[0.000206508, -0.349092, 0.937089 , 0.00146275] - trans_champs.setSFVec3f(INITIAL_trans) - rot_champs.setSFRotation(INITIAL_rot) - -#Fonction de l'action à prendre -def rouler (action) : - if action == 1 : - conduire.setSteeringAngle(-0.4) - conduire.setCruisingSpeed(0.8) - elif action == 2 : - conduire.setSteeringAngle(-0.1) - conduire.setCruisingSpeed(0.8) - elif action == 3 : - conduire.setSteeringAngle(0) - conduire.setCruisingSpeed(0.8) - elif action == 4 : - conduire.setSteeringAngle(0.1) - conduire.setCruisingSpeed(0.8) - elif action == 5 : - conduire.setSteeringAngle(0.4) - conduire.setCruisingSpeed(0.8) - elif action == 6 : - conduire.setSteeringAngle(-0.4) - conduire.setCruisingSpeed(1.8) - elif action == 7 : - conduire.setSteeringAngle(-0.1) - conduire.setCruisingSpeed(1.8) - elif action == 8 : - conduire.setSteeringAngle(0) - conduire.setCruisingSpeed(1.8) - elif action == 9 : - conduire.setSteeringAngle(0.1) - conduire.setCruisingSpeed(1.8) - elif action == 10 : - conduire.setSteeringAngle(0.4) - conduire.setCruisingSpeed(1.8) - - -#Vérification de l'état de la voiture -#Retourner valeur [distance angle] -def observe(lidar_sensor) : - try : - tablo = lidar_sensor.getRangeImage() - etat = np.divide(np.array(tablo),10) - except : - print("pas de retour du lidar") - etat = np.zeros(36) - return np.array(etat).astype('float32') - -#Fonction pour detection de collision -def evaluer(): - recompense = 0 - avant = capteur_avant.getValue() - gauche = capteur_gauche.getValue() - droite = capteur_droite.getValue() - balise = capteur_balise.getValue() - - if avant >= 900 : - print("Collision avant") - initialisation() #Réinitialisation de position en cas de crash - recompense = -10 - done = True - elif (avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937 : - print("Collision gauche") - initialisation() - recompense = -10 - done = True - elif (avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961 : - print("Collision droite") - initialisation() - recompense = -10 - done = True - elif balise > 700 : - done = False - print("Balise passée") - recompense = 10 - else : - done = False - recompense = 0 - - return recompense - -#Fonction épisode -def is_done() : - if not done : - return False - else : - return True - -#Affichage -def view() : - pass - -#Programme principal -# while conduire.step() != -1: -# -# etat = observe(lidar) -# if etat[4]= 900 : - print("Collision avant") - self.reset() #Réinitialisation de position en cas de crash - recompense = -10 - done = True - elif (avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937 : - print("Collision gauche") - self.reset() - recompense = -10 - done = True - elif (avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961 : - print("Collision droite") - self.reset() - recompense = -10 - done = True - elif balise > 700 : - done = False - print("Balise passée") - recompense = 10 - else : - done = False - recompense = 0 - return recompense, done - - def step(self, action): - if action == 1 : - self.conduire.setSteeringAngle(-0.4) - self.conduire.setCruisingSpeed(0.8) - elif action == 2 : - self.conduire.setSteeringAngle(-0.1) - self.conduire.setCruisingSpeed(0.8) - elif action == 3 : - self.conduire.setSteeringAngle(0) - self.conduire.setCruisingSpeed(0.8) - elif action == 4 : - self.conduire.setSteeringAngle(0.1) - self.conduire.setCruisingSpeed(0.8) - elif action == 5 : - self.conduire.setSteeringAngle(0.4) - self.conduire.setCruisingSpeed(0.8) - elif action == 6 : - self.conduire.setSteeringAngle(-0.4) - self.conduire.setCruisingSpeed(1.8) - elif action == 7 : - self.conduire.setSteeringAngle(-0.1) - self.conduire.setCruisingSpeed(1.8) - elif action == 8 : - self.conduire.setSteeringAngle(0) - self.conduire.setCruisingSpeed(1.8) - elif action == 9 : - self.conduire.setSteeringAngle(0.1) - self.conduire.setCruisingSpeed(1.8) - elif action == 10 : - self.conduire.setSteeringAngle(0.4) - self.conduire.setCruisingSpeed(1.8) - obs = self.observe() - print(obs) - print(np.shape(obs)) - reward,done = self.evaluer() - return obs, reward, done, {} - - def render(self, mode="human", close=False): - pass - -def main() : - env = WebotsGymEnvironment() - check_env(env) - -# # Entrainnement - model = PPO('MlpPolicy', env, n_steps=2048, verbose=1) - model.learn(total_timesteps=1e5) - - obs = env.reset() - - for _ in range(20): - #Prédiction pour séléctionner une action à partir de l'observation - action, _states = model.predict(obs, deterministic=True) - obs, 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z$XZ1Qn`m;`#E^34H_B+%-}Qd})bU}78H>dy{-_-~;`kSi<>MLQDTrXPCb3#2x=)wV zU-_gml%1w=V$GCCSabxxfF6;a;Q{<2@z`Mz{-P|d>5f@4x0l7@6aV%(~HY}gMR^cl$PRv4EYzP+`77-8}5aSyz%HiLd`ESXK= 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/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Jessica/controller_Jessica.py b/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Jessica/controller_Jessica.py deleted file mode 100644 index 6d08559..0000000 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Jessica/controller_Jessica.py +++ /dev/null @@ -1,196 +0,0 @@ -import numpy as np -import random -import sys -import gym -import numpy as np -from controller import Supervisor -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 - -class OpenAIGymEnvironment(Supervisor, gym.Env): - def __init__(self, max_episode_steps=1000): - super().__init__() - self.lidar = Lidar("lidar") - self.action_space = gym.spaces.Discrete(10) - min = np.zeros(36) - max = np.ones(36) - self.observation_space = gym.spaces.Box(min, max, dtype=np.float32) - self.state = None - self.spec = gym.envs.registration.EnvSpec(id='WebotsEnv-v0', max_episode_steps=max_episode_steps) - - # Environment specific - self.__timestep = int(self.getBasicTimeStep()) - - #Paramètre de la voiture (position, etc..) - self.robot = self.getSelf() - self.voiture_robot = super().getFromDef("vehicle") - self.trans_champs = self.voiture_robot.getField("translation") - self.rot_champs = self.voiture_robot.getField("rotation") - - # Observation - def observe(self): - - try : - tablo = self.lidar.getRangeImage() - etat = np.divide(np.array(tablo),10) - except : - print("pas de retour du lidar") - etat = np.zeros(36) - print('etat=',etat) - return np.array(etat).astype('float32') - - def reset(self): - # Reset the simulation - self.simulationResetPhysics() - self.simulationReset() - super().step(self.__timestep) - - self.INITIAL_trans = [-3, 4, 0.0195182] - self.INITIAL_rot=[0.000206508, -0.349092, 0.937089 , 0.00146275] - self.trans_champs.setSFVec3f(self.INITIAL_trans) - self.rot_champs.setSFRotation(self.INITIAL_rot) - - - #lidar - - self.lidar.enable(self.__timestep) - self.lidar.enablePointCloud() - - #Capteurs - #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.__timestep) - self.capteur_gauche.enable(self.__timestep) - self.capteur_droite.enable(self.__timestep) - - #Capteur de balise - self.capteur_balise = super().getDevice('capteur_balise') - self.capteur_balise.enable(self.__timestep) - - super().step(self.__timestep) - obs = self.observe() - print ('obs=',obs) - return obs - - - # Reward - def evaluer(self): - - recompense = 0 - avant = self.capteur_avant.getValue() - gauche = self.capteur_gauche.getValue() - droite = self.capteur_droite.getValue() - balise = self.capteur_balise.getValue() - - if avant >= 900 : - print("Collision avant") - self.reset() #Réinitialisation de position en cas de crash - recompense = -10 - done = True - elif (avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937 : - print("Collision gauche") - self.reset() - recompense = -10 - done = True - elif (avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961 : - print("Collision droite") - self.reset() - recompense = -10 - done = True - elif balise > 700 : - done = False - print("Balise passée") - recompense = 10 - else : - done = False - recompense = 0 - return recompense, done - - def step(self, action): - if action == 1 : - super().step(self.__timestep) -# super().setSteeringAngle(-0.4) -# super().setCruisingSpeed(0.8) - - elif action == 2 : - super().step(self.__timestep) -# super().setSteeringAngle(-0.1) -# super().setCruisingSpeed(0.8) - elif action == 3 : - super().step(self.__timestep) -# super().setSteeringAngle(0) -# super().setCruisingSpeed(0.8) - elif action == 4 : - super().step(self.__timestep) -# super().setSteeringAngle(0.1) -# super().setCruisingSpeed(0.8) - elif action == 5 : - super().step(self.__timestep) -# super().setSteeringAngle(0.4) -# super().setCruisingSpeed(0.8) - elif action == 6 : - super().step(self.__timestep) -# super().setSteeringAngle(-0.4) -# super().setCruisingSpeed(1.8) - elif action == 7 : - super().step(self.__timestep) -# super().setSteeringAngle(-0.1) -# super().setCruisingSpeed(1.8) - elif action == 8 : - super().step(self.__timestep) -# super().setSteeringAngle(0) -# super().setCruisingSpeed(1.8) - elif action == 9 : - super().step(self.__timestep) -# super().setSteeringAngle(0.1) -# super().setCruisingSpeed(1.8) - elif action == 10 : - super().step(self.__timestep) -# super().setSteeringAngle(0.4) -# super().setCruisingSpeed(1.8) - -# super().step(self.__timestep) - obs = self.observe() - reward,done = self.evaluer() - - return obs, reward, done, {} - - -def main(): - # Initialize the environment - env = OpenAIGymEnvironment() - check_env(env) - - # Train - model = PPO('MlpPolicy', env, n_steps=2048, verbose=1) - model.learn(total_timesteps=15) - - obs = env.reset() - for _ in range(100000): - action, _states = model.predict(obs) - obs, reward, done, info = env.step(action) - print(obs, reward, done, info) - if done: - obs = env.reset() - - -if __name__ == '__main__': - main() - - - - - - - - - - - - diff --git a/Simulateur/Readme.md b/Simulateur/Readme.md deleted file mode 100644 index 22df554..0000000 --- a/Simulateur/Readme.md +++ /dev/null @@ -1,19 +0,0 @@ -# Démarrer avec Webots -Plusieurs équipes utilisent le logiciel de simulation Webots version R2023b pour la saison 2023-2024. - -Pour commencer avec Webots, il est bon de lire au minimum le guide de démarrage rapide : -https://www.cyberbotics.com/doc/guide/getting-started-with-webots - -Et de faire au minimum les tutoriaux 1, 2, 3, 4, 5 et 8 : -https://www.cyberbotics.com/doc/guide/tutorials - -Le dossier compressé _Simulateur_CoVAPSy_Webots2023b_Base.zip_ contient une piste simple avec 3 voitures TT-02 (un sparring partner, une voiture programmée en C et une en python) ainsi que des éléments pour faire des pistes différentes. - -Pour modifier la piste. Le premier groupe contient les 6 éléments _solid_ de base (droit_1m, droit_50_cm et virage, en vert et en rouge) : - -* Copier/Coller du premier groupe au second permet d'ajouter un élément -* Une fois le _solid_ sélectionné dans la vue 3D, les flèches verte et rouge permettent de le déplacer. La flèche "rotation" bleue permet de le faire tourner. -* Après avoir positionné l'élément grossièrement sur la piste, le champ _translation_ permet de le positionner finement. Un multiple de 0,25 m dans les champs x et y permet un bon alignement des éléments. - -Pour partager une nouvelle piste, envoyer le fichier wbt à anthony.juton@ens-paris-saclay.fr - diff --git a/Simulateur/SimulateurMatlabCentraleSupelec/README.md b/Simulateur/SimulateurMatlabCentraleSupelec/README.md deleted file mode 100644 index 52117dc..0000000 --- a/Simulateur/SimulateurMatlabCentraleSupelec/README.md +++ /dev/null @@ -1 +0,0 @@ -# Simulateur de Centrale diff --git a/Simulateur/SimulateurWebotsENS2022/ImageToStl.com_bloc_0.5m.obj b/Simulateur/SimulateurWebotsENS2022/ImageToStl.com_bloc_0.5m.obj deleted file mode 100644 index e99faeb..0000000 --- a/Simulateur/SimulateurWebotsENS2022/ImageToStl.com_bloc_0.5m.obj +++ /dev/null @@ -1,822 +0,0 @@ -# File generated by ImageToStl.com - Free Image and 3D model conversion tools - -v -0.012437 0.214842 0.330319 -v -0.012437 0.214842 0.333319 -v -0.012437 0.414842 0.330319 -v -0.012437 0.414842 0.333319 -v 0.487563 0.214842 0.330319 -v 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load_trajectory -from collections import deque - -from vehicle import Driver -from controller import Lidar -from controller import Field -from controller import Supervisor - - - -# --------------GYM---------------------------- - -class CarLogic(Driver): - """ - Abstract class handling recurrent driving logic and logging. - - One should create a class inherited from CarLogic and implement a method drive() that takes arbitrary input and - uses the `setSpeedCommand()` and `setSteeringCommand()` to control the car. - A minimal working example is available at `minimalController/minimalController.py` - - The class is far from perfect, be careful ! - """ - - def __init__(self, - reset_delay=12, - record_list = ('steering_command','lidar_range_image','speed_command','car_position'), - path_to_trajectory=None, - rot_value=[-0.000257, 0.000618, 1, -0.784], - pos_value=[1.5, 3.74, 0.0195182]): - print('Initialisation') - 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() - - # Paramètre de la voiture (position, etc..) - self.car = super().getFromDef("vehicle") - self.trans_champs = self.car.getField("translation") - self.rot_champs = self.car.getField("rotation") - - # Capteur de pression - list_touch_sensor_name = ['touch_sensor_right','touch_sensor_back','touch_sensor_front','touch_sensor_left'] - - # Look, super() need to be provided with explicit parameters here - self.touch_sensors = {sensor_name:super(CarLogic,self).getDevice(sensor_name) for sensor_name in list_touch_sensor_name} - - [sensor.enable(self.sensorTime) for sensor in self.touch_sensors.values()] - - self.reset_delay = reset_delay - - #distance - - self.last_position = None - self.travelled_distance = 0 - - - self.record_list = record_list - self.init_full_logs() - - self.setSpeedCommand(0) - self.setSteeringCommand(0) - - # Capteur de balise - self.init_balises() - - - self.init_spawn(path = path_to_trajectory, rot_value=rot_value, pos_value=pos_value) - - def init_empty_buffer(self, buffer_size=3): - """ - - :param buffer_size: - :return: - """ - self.steeringCommand_buffer = deque([], maxlen=buffer_size) - self.speedCommand_buffer = deque([], maxlen=buffer_size) - self.velocity_buffer = deque([], maxlen=buffer_size) - - def record_kv_episode_log(self,key,value): - """ - Add the value to the corresponding temporary key list. - """ - if key in self.record_list: - self.episode_log_buffer[key].append(value) - - def reset_n_write_log(self): - """ - - TODO: Problem here if some value is not recorded in certain episods () - TODO: write to disk every x iterations (around 40) to prevent data issues - """ - for k,v in self.episode_log_buffer.items(): - self.full_logs[k].append(v) - - self.episode_log_buffer = {k:[] for k in self.record_list} - - def init_full_logs(self): - """ - helper function to initialize the log tracking system - :return: - """ - self.full_logs = {k:[] for k in self.record_list} - self.episode_log_buffer = {k:[] for k in self.record_list} - - def init_balises(self): - """ - DEPRECATED - useful when implementing lap reward. - """ - - self.capteur_balise = super().getDevice('capteur_balise') - self.capteur_balise.enable(self.sensorTime) - balises = [] - loop = True - i = 1 - while loop: - balise = self.getFromDef(f'Balise({i})') - - if balise is None: - break - coord = balise.getPosition() - balises.append((coord[0], coord[1])) - i += 1 - self.balises = balises - self.current_advancement = set() - - print('balises: ',self.balises) - - def init_spawn(self,rot_value,pos_value,path=None): #TODO: horrible one, change that - if path: - print(path) - traj = load_trajectory(path) - self.spawn_position = traj['positions'] - self.spawn_rotation = traj['rotations'] - else: - self.spawn_position = pos_value - self.spawn_rotation = rot_value - - # Remise à 0 - def respawn(self,speedCommand=0,steeringCommand=0): - """ - Respawn can be random or fixed depending on how self.spawn_position was defined. - """ - if not isinstance(self.spawn_position[0], list): - xp,yp,zp = self.spawn_position - xr, yr, zr,kr = self.spawn_rotation - else: - k = random.randint(0,len(self.spawn_position)-1) - xp, yp, zp = self.spawn_position[k] - xr, yr, zr, kr = self.spawn_rotation[k] - - INITIAL_trans = [xp + random.uniform(-0.05,0.05),yp + random.uniform(-0.05,0.05),zp] - INITIAL_rot = [xr,yr,zr,kr] - - self.trans_champs.setSFVec3f(INITIAL_trans) - self.rot_champs.setSFRotation(INITIAL_rot) - self.car.setVelocity([0,0,0,0,0,0]) - self.current_advancement = set() - - self.setSteeringCommand(steeringCommand) - self.setSpeedCommand(speedCommand) - - def reset(self, speedCommand=0, steeringCommand=0): - """ - Reset the car and call the respawn function - """ - self.reset_n_write_log() - self.respawn(speedCommand=speedCommand,steeringCommand=steeringCommand) - for i in range(self.reset_delay): - super().step() - x,y,_ = self.car.getPosition() - self.last_position = (x,y) - - - def setSpeedCommand(self,speed_command): - """ - Set the speed command of the car. Record the value if needed - """ - super().setCruisingSpeed(speed_command) - - self.speedCommand = speed_command - self.record_kv_episode_log('speed_command',speed_command) - - - def setSteeringCommand(self,steeringCommand): - """ - Set the steering command of the car. Record the value if needed - """ - super().setSteeringAngle(steeringCommand) - self.steeringCommand = steeringCommand - self.record_kv_episode_log('steering_command',steeringCommand) - - def get_square_speed(self): - """ - Get the squared speed of the car - :return: - """ - return (np.array(self.car.getVelocity()[:3])**2).sum() - - def get_speed_vector(self,normalized = False): - """ - Get the speed vector of the car along x y z. - :param normalized: - :return: - """ - if normalized: - return (np.array(self.car.getVelocity()[:3])+1)/2 - else: - return np.array(self.car.getVelocity()[:3]) - - def get_normalized_lidar_range_image(self): - """ - Return the lidar range image with value between 0 and 1. inf values are set to 1. - """ - tableau = self.lidar.getRangeImage() - etat = np.divide(np.array(tableau), 10) - etat[~np.isfinite(etat)] = 1 - self.record_kv_episode_log('lidar_range_image', etat.tolist()) - - return etat - - def update_travelled_distance(self): - """ - A recorder for the total travelled distance. Also usefull to update last position - """ - x,y,_ = self.car.getPosition() - self.record_kv_episode_log('car_position', (x,y)) - xp,yp = self.last_position - self.travelled_distance += np.sqrt((x-xp)**2+(y-yp)**2) - self.last_position = (x,y) - - def get_collision(self): - """ - Test all touch sensors to detect a possible collision - :return: - """ - for name,sensor in self.touch_sensors.items(): - if sensor.getValue() != 0.0: - print(f"Colision detected : {name}") - return True - return False - - def check_bug(self): - """ - Verify if something strange is happening physic wise. If so, return True to reset the simulation. - :return: - """ - x, y, _ = self.car.getPosition() - xp, yp = self.last_position - bug_threshold = 20 - if np.sqrt((x-xp)**2+(y-yp)**2) > bug_threshold: - return True - - bug_velocity_threshold = 40 #TODO: parametrize this at class level - - if (np.array(self.car.getVelocity()[:3])**2).sum()>bug_velocity_threshold: - return True - return False - - def get_balise(self): - """ - DEPRECATED - useful when implementing lap reward. - """ - balise = self.capteur_balise.getValue() - if balise > 700: - return self.get_balise_id() - return None - - def update_advancement(self, id): - """ - DEPRECATED - useful when implementing lap reward. - """ - if not id is None and id not in self.current_advancement: - self.current_advancement.add(id) - if len(self.current_advancement) >= len(self.balises): - self.current_advancement = set() - print("Un tour !") - return True - else: - print(f"Balise {id} passée") - return True - return False - - def render(self, mode="human", close=False): - pass - - def get_balise_id(self): - """ - DEPRECATED - useful when implementing lap reward. - """ - car_pos = self.car.getPosition() - dist = list(map(lambda x: (x[0]-car_pos[0])**2 + (x[1]-car_pos[1])**2, self.balises)) - return dist.index(min(dist)) - - -def main(): - pass - -if __name__ == '__main__': - main() - diff --git a/Simulateur/Webots-Efrei-2023/controllers/minimalController/minimalController.py b/Simulateur/Webots-Efrei-2023/controllers/minimalController/minimalController.py deleted file mode 100644 index 34591b7..0000000 --- a/Simulateur/Webots-Efrei-2023/controllers/minimalController/minimalController.py +++ /dev/null @@ -1,65 +0,0 @@ -import os - -import numpy as np - - -import sys -sys.path.append("../") -from car_logic import CarLogic - - -class MinimalController(CarLogic): - """ - An example of a basic driving logic implemented on top of the CarLogic Class. - """ - def __init__(self,path_to_trajectory=None): - super().__init__( path_to_trajectory=path_to_trajectory) - - def drive(self): - - id_balise = self.get_balise() - - self.update_advancement(id_balise) - self.update_travelled_distance() - xy_lidar = list(map(lambda x: (x.x, x.y), self.lidar.getPointCloud())) - y_lidar = list(map(lambda x: x.y, self.lidar.getPointCloud())) - - dist = list(map(lambda p: p[0] ** 2 + p[1] ** 2, xy_lidar)) - front_dist = np.array(dist[-8:] + dist[:8]) - min_front_dist = front_dist[np.isfinite(front_dist)].min() - id = dist.index(min_front_dist) - - if id in [0, 1, len(dist), len(dist) - 1]: - self.setSpeedCommand(-0.5) - self.setSteeringCommand(-0.1) - elif y_lidar[id] < 0: - self.setSpeedCommand(0.5) - self.setSteeringCommand(-0.4) - else: - self.setSpeedCommand(0.5) - self.setSteeringCommand(0.4) - - - def step(self): - self.drive() - super().step() - -def main(): - - print(os.getcwd()) - path_to_trajectory = r"../../worlds/trajectories/Circuit-trajectory.json" - - carController = MinimalController(path_to_trajectory =path_to_trajectory) - - print("It's lights out and away we go !") - for i in range(2): - carController.reset() - for _ in range(7000): - carController.step() - print("Reseting at random position") - print("Ending...") - print(carController.full_logs) - -if __name__ == '__main__': - main() - diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-310.pyc b/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-310.pyc deleted file mode 100644 index fbfc71b93b40f921834d7bed94ab682f65181306..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 517 zcmYjOy-ve05VjpxDkT*i7`OTOCjy5}5Vs79BaRiCAWnF6gC>~c zF-qtgAZRzo!TeAJwhb}@aYIlZ&EW1ex$Vud#}8zNAEHO}i09-0UZud-5k@mIMr+8e zTwsg>AA*EfLUwCx6IxSK3SHAvXLQ|ChH+O@D;Q6rNnA32l6jLgAfDD8Z`rBHy|GtM z$Syz=AJ3!pbl(ZS5B6;fxK$#aqY zVd-G7T6w8LcfXwv%By-Q44VDLTH#Y;P%-Y|&KD)UPfF|}1QKE?n8t4={-?s4>-I)r Pc%{TJEX#%5Hj3#tER~H( diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-38.pyc b/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/trajectory.cpython-38.pyc deleted file mode 100644 index c08ffadd883250558920c6852f8a162cdf584da9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 471 zcmYjO!Ab)$5KXdMskOz62S33*w4S_(h@uE07PU&H1eUl-+mvjwB&oE}p7i9!UueNg ze<@c_{(>iyMQ8`+y_w8RCX;z;G-|-h4gbhip1*^$Dabo#Ud^5d3Mdqy2PmS^JLo|Q zJ0OXFd<3*J80cRj&)Z%vyqW_K7R-F@8~WUvLkkyZ1{cv1Ea4n=!4bFz58xThkiQMJ z9n?weVpTBd;*n9(RfHncxL7knlc-lOh#jU@rDYKIUCnATVwqKXx&k`%{r$Q+#+Sqr zd`l)g=Ylhd@3ab82D3@QG|!o|>z))uSfT^qIz8_M)um@O&(YXkIp@9Ve diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-310.pyc b/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-310.pyc deleted file mode 100644 index f5bdcfee732cf665ec8236ccfa3e98db4a6d754c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 873 zcmZ8f!H&}~5OvaS6{=Po;EH6yr8%^LmAF)^6$pfcw1R32LP9~6lQ^xljvb913e|Gj zm013P6DRf;_>^4v3r={Fc9+GHCyqVy=FPm(!(pGe_2a|$bmSBA8-M;??v6Zh+YFq^ zNamfB_ayV@Buk5V?+%t+5bK@!aLf7^y6W!|SEIMtG%#4_4D=GXF?21Yq!~|vJKi+M z`X13y!k0o}143#weRvhNrCM&IcPv{Ph&#Qei1e=?E?xdQ*acpC_3%c$fVBVod^U~W z8V)A5psOT)&drI{WxVEzwq~5^^n}B>ZD-6sl^jIDl^s8Q8C(4Zm@+ws_+8^Y7eyr* zHp#@mg~~Ogyz$$p*s1%%2xTl&DX%+D(t58EIP{_G5TJ(!n*cbv tu;)yD`#(&4ZY1sZ0&n1V13(6^La+I@(G5gn8i?Sa&^Wq>O)PR9kiU{D@zMYQ diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-38.pyc b/Simulateur/Webots-Efrei-2023/controllers/utils/__pycache__/utils.cpython-38.pyc deleted file mode 100644 index 91e6b34187719f34c7d2a0d7eede917a487539fa..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 848 zcmZ8f&2G~`5Z-m#%8D8x0av6IT;fA)sKh0zDv(eGQh_MpM?#ju#$G3j*1OT{8cI}7 ztwcF;;}!Y}Jc_S80yj?V*hxznX~yf>nb~jVo7cTwmw0*q=10~K2>FFS|BCmKM_%o& zH#y1a8F@$YU_zo!M!jnz7}r^=z3UH-oD)Y+1Gwpeb8A8qNiCh^YWk6@N(@=QP=-5ZfLRAo z8wkQxpz*HKnUhAh{%hUiBya+^yxP#46W{%9;(Mg4RZ|B09D$jGu$Fz2O0isIAUtkX zV1Ex-eIh%^(r2tt9?FNsZrOvzpu5BS8eJf_h*@-MG#c?P$1tE1| zAQMgWhRlHj?uh}LW=1rB)g&t1LUFc}4~~v`u?1q2{UPQF{w%JXI#xy0u{nFr$9-!;DSMnE;;OGVb diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/trajectory.py b/Simulateur/Webots-Efrei-2023/controllers/utils/trajectory.py deleted file mode 100644 index 25da04d..0000000 --- a/Simulateur/Webots-Efrei-2023/controllers/utils/trajectory.py +++ /dev/null @@ -1,12 +0,0 @@ -import json - - - -def load_trajectory(path_to_json): - with open(path_to_json, 'r') as f: - trajectory = json.loads(f.read()) - return trajectory - - - - diff --git a/Simulateur/Webots-Efrei-2023/controllers/utils/utils.py b/Simulateur/Webots-Efrei-2023/controllers/utils/utils.py deleted file mode 100644 index cac276a..0000000 --- a/Simulateur/Webots-Efrei-2023/controllers/utils/utils.py +++ /dev/null @@ -1,18 +0,0 @@ -def linear_schedule(initial_value): - """ - Linear learning rate schedule. From https://github.com/araffin/rl-baselines-zoo/blob/master/utils/utils.py#L225 - :param initial_value: (float or str) - :return: (function) - """ - if isinstance(initial_value, str): - initial_value = float(initial_value) - - def func(progress): - """ - Progress will decrease from 1 (beginning) to 0 - :param progress: (float) - :return: (float) - """ - return progress * initial_value - - return func \ No newline at end of file diff --git a/Simulateur/Webots-Efrei-2023/worlds/.Circuit.wbproj b/Simulateur/Webots-Efrei-2023/worlds/.Circuit.wbproj deleted file mode 100644 index 32db898..0000000 --- a/Simulateur/Webots-Efrei-2023/worlds/.Circuit.wbproj +++ /dev/null @@ -1,9 +0,0 @@ -Webots Project File version R2022b -perspectives: 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-0.6833946910488269], [-0.00029914771415821703, -0.0005069250411661644, 0.9999998267688088, -0.7950886922219347], [-0.0003862426136295257, -4.505579158065147e-05, 0.9999999243933068, -0.9068520370980817], [-0.00014911529644379417, -7.496423648512271e-05, 0.9999999860724957, -1.1282676189037764], [-0.0001134903701903091, -5.488826622220113e-05, 0.9999999920536071, -1.3718078390322148], [0.00031576956580987305, -8.225308564064059e-05, 0.9999999467620042, -1.4563108943461343]]} \ No newline at end of file diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 b/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 similarity index 100% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 rename to Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 b/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 similarity index 100% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 rename to Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 b/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 similarity index 100% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 rename to Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 b/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 similarity index 100% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 rename to Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 b/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 similarity index 100% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 rename to Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/controller_Gym.py b/Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py similarity index 52% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/controller_Gym.py rename to Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py index eda5549..127fd48 100644 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/controllers/controller_Gym_20221011/controller_Gym.py +++ b/Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py @@ -1,6 +1,6 @@ -"""Voiture autonome avec utilisation d'un +"""Voiture autonome avec utilisation d"un LIDAR sur WEBOTS -Auteur : Chrysanthe et Jessica +Auteur: Chrysanthe et Jessica """ import numpy as np @@ -10,6 +10,7 @@ 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 @@ -19,60 +20,59 @@ #--------------GYM---------------------------- -#Création de l'environnement GYM -class WebotsGymEnvironment(Driver, gym.Env) : - def __init__(self): - super().__init__() #Objet héritant la classe Driver +#Création de l"environnement GYM +class WebotsGymEnvironment(gym.Env): + def __init__(self, vehicle): + #Initialisation du driver + self.driver = Driver() - basicTimeStep = int(super().getBasicTimeStep()) - self.sensorTime = basicTimeStep//4 + basicTimeStep = int(self.driver.getBasicTimeStep()) + self.sensorTime = basicTimeStep // 4 + #Paramètre de la voiture (position, etc..) + self.vehicle = vehicle #self.driver.getFromDef("vehicle") + print(self.vehicle) + print(self.driver.__dict__) #Lidar - self.lidar = Lidar("lidar") - - #Capteur LIDAR + self.lidar = self.driver.getDevice("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 = 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 = super().getDevice('capteur_balise') + self.capteur_balise = self.driver.getDevice("capteur_balise") self.capteur_balise.enable(self.sensorTime) - #Vérification de l'état de la voiture - def observe(self) : - try : + 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.vehicle.getField("translation") + self.rot_champs = self.vehicle.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.divide(np.array(tableau),10) - except : #En cas de non retour lidar + 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()) + etat = np.zeros(self.lidar.getNumberOfPoints(), dtype=np.float32) - return np.array(etat).astype('float32') + return etat - #Remise à 0 - def initialisation(self) : + #Remise à 0 pour l"environnement GYM + def reset(self, seed=0): #self.capteur_avant.disable() #self.capteur_gauche.disable() #self.capteur_droite.disable() @@ -81,9 +81,9 @@ def initialisation(self) : x = random.uniform(1.5, 1.65) y = random.uniform(3.66, 3.8) - #Fonction d'initialisation + #Fonction d"initialisation INITIAL_trans = [x, y, 0.0195182] - INITIAL_rot=[-0.000257, 0.000618, 1 , -0.784] + INITIAL_rot = [-0.000257, 0.000618, 1 , -0.784] self.trans_champs.setSFVec3f(INITIAL_trans) self.rot_champs.setSFRotation(INITIAL_rot) @@ -92,100 +92,97 @@ def initialisation(self) : #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, seed=0): - self.initialisation() #Retour état obs = self.observe() #super().step() info = {} return obs, info - #Fonction pour detection de collision et attribution des récompenses - def evaluer(self): - recompense = 0 + #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) : + if avant >= 900 and not(done): print("Collision avant") - recompense = -100 + reward = -100 done = True - elif ((avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937) and not(done) : + elif ((avant >= 854 and gauche >= 896) or (avant >= 696 and gauche >= 910) or gauche >= 937) and not(done): print("Collision gauche") - recompense = -100 + reward = -100 done = True - elif ((avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961) and not(done) : + elif ((avant >= 850 and droite >= 893) or (avant >= 584 and droite >= 910) or droite >= 961) and not(done): print("Collision droite") - recompense = -100 + reward = -100 done = True - elif balise > 700 : + elif balise > 700: done = False print("Balise passée") - recompense = 20 - else : + reward = 20 + else: done = False - recompense = 0 - - return recompense, done, False # we never truncate for now but maybe in the future ? - - #Fonction pou r 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) + reward = 0 - elif action == 3 : - super().setSteeringAngle(0.1) + self.driver.step() - elif action == 4 : - super().setSteeringAngle(0.4) - - #Fonction step de l'environnement GYM - def step(self, action): - self.rouler(action) + return obs, reward, done, truncated, {} - obs = self.observe() + #Fonction render de l"environnement GYM + def render(self, mode="human", close=False): + pass - reward, done, truncated = self.evaluer() - super().step() - return obs, reward, done, truncated, {} +def create_vehicles(n): + """ + copies the default DEF vehicule Robot n_envs times so that they can be controlled independently + """ + supervisor = Supervisor() + node = supervisor.getFromDef("vehicle") + children_field = node.getParentNode().getField("children") + print(children_field.__dict__) + vehicles = [] - #Fonction render de l'environnement GYM - def render(self, mode="human", close=False): - pass + for i in range(n): + # create a copy of the old node but with a new name + index = children_field.importMFNodeFromString(-1, node.exportString()) + node = children_field.getMFNode(index) + vehicles.append(node) + return vehicles #----------------Programme principal-------------------- -def main() : - env = WebotsGymEnvironment() +def main(): + n = 1 # number of vehicles + #vehicles = create_vehicles(n) + # env = SubprocVecEnv([lambda: WebotsGymEnvironment(i) for i in range(n_envs)]) + vehicles = [Supervisor().getFromDef("vehicle")] + env = WebotsGymEnvironment(vehicles[0]) 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) + model = PPO("MlpPolicy", env, + n_steps=2048, + n_epochs=10, + batch_size=32, + learning_rate=3e-3, + verbose=1, + device="cuda:0" + ) #Entrainnement model.learn(total_timesteps=1e6) @@ -195,19 +192,18 @@ def main() : #del model - #Chargement des données d'apprentissage + #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 + #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__' : +if __name__ == "__main__": main() - diff --git a/Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py b/Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py new file mode 100644 index 0000000..e69de29 diff --git a/Simulateur/protos/car.proto b/Simulateur/protos/car.proto new file mode 100644 index 0000000..06be574 --- /dev/null +++ b/Simulateur/protos/car.proto @@ -0,0 +1,894 @@ +PROTO vehicle [ + field SFString name "vehicule" +] Robot { + translation 1.49768 3.85153 0.0209979 + rotation -0.000257203942361522 0.0006180928614876137 0.9999997759036483 -0.7840253071795864 + children [ + Transform { + translation 0.1 0.04 0 + rotation 0 0 1 -1.5708 + children [ + DEF LEFT_STEER_HINGE2 Hinge2Joint { + jointParameters HingeJointParameters { + axis 0 0 -1 + suspensionSpringConstant 100000 + suspensionDampingConstant 4000 + suspensionAxis 0 0 1 + } + jointParameters2 JointParameters { + axis -1 0 0 + dampingConstant 5 + } + device [ + RotationalMotor { + name IS name + "_left_steer" + minPosition -1.34 + maxPosition 1.34 + maxTorque 10000 + sound "" + } + PositionSensor { + name IS name + "_left_steer_sensor" + } + ] + device2 [ + RotationalMotor { + name IS name + "_left_front_wheel" + acceleration 694.4444444444445 + maxVelocity 29.799999999999997 + maxTorque 100000000 + sound "" + } + PositionSensor { + name IS name + "_left_front_sensor" + } + Brake { + name IS name + "_left_front_brake" + } + ] + endPoint Slot { + type "vehicle wheel" + endPoint Slot { + type "vehicle wheel" + endPoint Solid { + rotation 0 1 0 0 + children [ + DEF WHEEL_SLOT Group { + } + Transform { + children [ + DEF TIRE Shape { + appearance PBRAppearance { + baseColorMap ImageTexture { + url [ + "https://raw.githubusercontent.com/cyberbotics/webots/R2022a/projects/vehicles/protos/abstract/textures/tire_texture_base_color.jpg" + ] + } + roughness 0.8 + roughnessMap ImageTexture { + url [ + "https://raw.githubusercontent.com/cyberbotics/webots/R2022a/projects/vehicles/protos/abstract/textures/tire_texture_roughness.jpg" + ] + } + metalness 0 + normalMap ImageTexture { + url [ + "https://raw.githubusercontent.com/cyberbotics/webots/R2022a/projects/vehicles/protos/abstract/textures/tire_texture_normal.jpg" + ] + } + occlusionMap ImageTexture { + url [ + "https://raw.githubusercontent.com/cyberbotics/webots/R2022a/projects/vehicles/protos/abstract/textures/tire_texture_occlusion.jpg" + ] + } + } + geometry IndexedFaceSet { + coord Coordinate { + point [ + 0.0075 -0.017 0, 0.0075 -0.016673349766854917 0.0033165354742741805, 0.0075 -0.015705952052691877 0.0065056183502065264, 0.0075 -0.01413498340914327 0.009444693961333238, 0.0075 -0.01202081528017131 0.012020815280171307, 0.0075 -0.00944469396133324 0.01413498340914327, 0.0075 -0.006505618350206527 0.015705952052691877, 0.0075 -0.003316535474274182 0.016673349766854917, 0.0075 -1.0409154007490208e-18 0.017, 0.0075 0.0033165354742741796 0.016673349766854917, 0.0075 0.006505618350206526 0.015705952052691877, 0.0075 0.009444693961333234 0.014134983409143273, 0.0075 0.012020815280171307 0.01202081528017131, 0.0075 0.014134983409143272 0.009444693961333238, 0.0075 0.015705952052691877 0.006505618350206529, 0.0075 0.016673349766854917 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572, 571, -1, 571, 550, 565, -1 + ] + creaseAngle 0.3 + } + } + ] + } + ] + } + ] + name IS name + model "Altino" + boundingObject Group { + children [ + Transform { + translation 0.03 0 0.017 + children [ + Box { + size 0.097 0.063 0.05 + } + ] + } + Transform { + translation 0.052 0 0.015 + children [ + Box { + size 0.16 0.09 0.04 + } + ] + } + Transform { + translation 0.052 0 0.015 + children [ + Box { + size 0.18 0.07 0.04 + } + ] + } + ] + } + physics Physics { + density -1 + mass 10 + centerOfMass [ + 0 0 0 + ] + } + radarCrossSection 100 + controller "controllerVehiculeDriver" + customData "0.1 0.08 0.08 0.02 0.02 700 5 E 0.1 1 1 250 150 0.1 0 0.25 3000 1000 2 4 -30 30 none " + supervisor TRUE + window "automobile" +} diff --git a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/.Piste.wbproj b/Simulateur/worlds/.Piste.wbproj similarity index 52% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/.Piste.wbproj rename to Simulateur/worlds/.Piste.wbproj index 55cb6d4..a8f6cd5 100644 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/.Piste.wbproj +++ b/Simulateur/worlds/.Piste.wbproj @@ -1,7 +1,7 @@ Webots Project File version R2023b -perspectives: 000000ff00000000fd00000002000000010000018d00000351fc0200000001fb0000001400540065007800740045006400690074006f00720100000017000003510000003f00ffffff0000000300000780000000a8fc0100000001fb0000001a0043006f006e0073006f006c00650041006c006c0041006c006c0100000000000007800000006900ffffff000005f10000035100000001000000020000000100000008fc00000000 -simulationViewPerspectives: 000000ff00000001000000020000014d000004a20100000002010000000100 -sceneTreePerspectives: 000000ff00000001000000030000001d00000314000000fa0100000002010000000200 +perspectives: 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2022/worlds/Piste.wbt b/Simulateur/worlds/car.wbo similarity index 99% rename from Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/Piste.wbt rename to Simulateur/worlds/car.wbo index c1ba719..14f91bc 100644 --- a/Simulateur/Apprentissage par renforcement Webots Gym StableBaselines 2022/worlds/Piste.wbt +++ b/Simulateur/worlds/car.wbo @@ -1,227 +1,3 @@ -#VRML_SIM R2022a utf8 -WorldInfo { -} -Viewpoint { - orientation -0.024245392713581615 -0.9992187330353125 0.031210326551783442 4.96162887255719 - position -2.8345822996678622 1.9573166415091952 10.331702690758817 -} -TexturedBackground { -} -TexturedBackgroundLight { -} -RectangleArena { - translation 0 1 0 - floorSize 7 9 - floorAppearance Parquetry { - type "dark strip" - } -} -Wall { - translation 0 3.9 0.2 - name "Balise" - size 0.01 0.7 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation -2 3.9 0.2 - name "Balise(6)" - size 0.01 0.6 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation 2.3 2.3 0.2 - rotation 0 0 1 -1.0471996938995747 - name "Balise(1)" - size 0.01 0.7 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation 1.7 3.7 0.2 - rotation 0 0 1 -1.0471996938995747 - name "Balise(7)" - size 0.01 0.7 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation 2.45 0.5 0.2 - rotation 0 0 1 -1.5707996938995747 - name "Balise(2)" - size 0.01 0.6 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation 2.15 -1.6 0.2 - name "Balise(3)" - size 0.01 0.9 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation 0.4 -1.6 0.2 - name "Balise(8)" - size 0.01 0.9 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation -1.1 -1.6 0.2 - name "Balise(9)" - size 0.01 0.9 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation -2.3 -1.5 0.2 - rotation 0 0 1 -0.7853996938995746 - name "Balise(4)" - size 0.01 1.1 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation -2 0.8 0.2 - rotation 0 0 1 -1.5707996938995747 - name "Balise(5)" - size 0.01 0.9 0.05 - appearance Roughcast { - colorOverride 0 0.666667 0 - textureTransform TextureTransform { - scale 2.4 1 - } - } -} -Wall { - translation -0.35 3.6 0 - rotation 0 0 1 -1.5707996938995747 - size 0.05 3.6 0.2 -} -Wall { - translation -0.03 -2 0 - rotation 0 0 1 -1.5707996938995747 - name "wall(7)" - size 0.05 5.3 0.2 -} -Wall { - translation -1.73472e-18 -1.2 0 - rotation 0 0 1 -1.5707996938995747 - name "wall(8)" - size 0.05 4.3 0.2 -} -Wall { - translation 1.8 2.65 0 - rotation 0 0 1 -2.7488996938995744 - name "wall(2)" - size 0.05 2 0.2 -} -Wall { - translation 2.15 0.3 0 - rotation 0 0 1 -3.141592653589793 - name "wall(5)" - size 0.05 3 0.2 -} -Wall { - translation 2.7 0.1 0 - rotation 0 0 1 -3.141592653589793 - name "wall(6)" - size 0.05 4.5 0.2 -} -Wall { - translation -2.7 -0.8 0 - name "wall(9)" - size 0.05 2.5 0.2 -} -Wall { - translation -2.8 2.9 0 - name "wall(15)" - size 0.05 2.8 0.2 -} -Wall { - translation -2.2 2.7 0 - name "wall(16)" - size 0.05 1.8 0.2 -} -Wall { - translation -2.15 -0.6 0 - name "wall(10)" - size 0.05 1.1 0.2 -} -Wall { - translation -1.9 0.3 0 - rotation 0 0 1 -0.5235996938995747 - name "wall(11)" - size 0.05 1 0.2 -} -Wall { - translation -2.5 0.6 0 - rotation 0 0 1 -0.5235996938995747 - name "wall(12)" - size 0.05 0.6 0.2 -} -Wall { - translation -2.6 1.2 0 - rotation 0 0 1 0.5236003061004253 - name "wall(13)" - size 0.05 0.9 0.2 -} -Wall { - translation -1.9 1.3 0 - rotation 0 0 1 0.5236003061004253 - name "wall(14)" - size 0.05 1.2 0.2 -} -Wall { - translation 2.2 3.3 0 - rotation 0 0 1 -2.7488996938995744 - name "wall(3)" - size 0.05 2 0.2 -} -Wall { - translation -0.5 4.2 0 - rotation 0 0 1 -1.5707996938995747 - name "wall(1)" - size 0.05 4.5 0.2 -} DEF vehicle Robot { translation 1.49768 3.85153 0.0209979 rotation -0.000257203942361522 0.0006180928614876137 0.9999997759036483 -0.7840253071795864 diff --git a/Simulateur/worlds/piste.wbt b/Simulateur/worlds/piste.wbt new file mode 100644 index 0000000..de0b218 --- /dev/null +++ b/Simulateur/worlds/piste.wbt @@ -0,0 +1,224 @@ +#VRML_SIM R2022a utf8 +WorldInfo { +} +Viewpoint { + orientation -0.024245392713581615 -0.9992187330353125 0.031210326551783442 4.96162887255719 + position -2.8345822996678622 1.9573166415091952 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scale 2.4 1 + } + } +} +Wall { + translation 2.45 0.5 0.2 + rotation 0 0 1 -1.5707996938995747 + name "Balise(2)" + size 0.01 0.6 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation 2.15 -1.6 0.2 + name "Balise(3)" + size 0.01 0.9 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation 0.4 -1.6 0.2 + name "Balise(8)" + size 0.01 0.9 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation -1.1 -1.6 0.2 + name "Balise(9)" + size 0.01 0.9 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation -2.3 -1.5 0.2 + rotation 0 0 1 -0.7853996938995746 + name "Balise(4)" + size 0.01 1.1 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation -2 0.8 0.2 + rotation 0 0 1 -1.5707996938995747 + name "Balise(5)" + size 0.01 0.9 0.05 + appearance Roughcast { + colorOverride 0 0.666667 0 + textureTransform TextureTransform { + scale 2.4 1 + } + } +} +Wall { + translation -0.35 3.6 0 + rotation 0 0 1 -1.5707996938995747 + size 0.05 3.6 0.2 +} +Wall { + translation -0.03 -2 0 + rotation 0 0 1 -1.5707996938995747 + name "wall(7)" + size 0.05 5.3 0.2 +} +Wall { + translation -1.73472e-18 -1.2 0 + rotation 0 0 1 -1.5707996938995747 + name "wall(8)" + size 0.05 4.3 0.2 +} +Wall { + translation 1.8 2.65 0 + rotation 0 0 1 -2.7488996938995744 + name "wall(2)" + size 0.05 2 0.2 +} +Wall { + translation 2.15 0.3 0 + rotation 0 0 1 -3.141592653589793 + name "wall(5)" + size 0.05 3 0.2 +} +Wall { + translation 2.7 0.1 0 + rotation 0 0 1 -3.141592653589793 + name "wall(6)" + size 0.05 4.5 0.2 +} +Wall { + translation -2.7 -0.8 0 + name "wall(9)" + size 0.05 2.5 0.2 +} +Wall { + translation -2.8 2.9 0 + name "wall(15)" + size 0.05 2.8 0.2 +} +Wall { + translation -2.2 2.7 0 + name "wall(16)" + size 0.05 1.8 0.2 +} +Wall { + translation -2.15 -0.6 0 + name "wall(10)" + size 0.05 1.1 0.2 +} +Wall { + translation -1.9 0.3 0 + rotation 0 0 1 -0.5235996938995747 + name "wall(11)" + size 0.05 1 0.2 +} +Wall { + translation -2.5 0.6 0 + rotation 0 0 1 -0.5235996938995747 + name "wall(12)" + size 0.05 0.6 0.2 +} +Wall { + translation -2.6 1.2 0 + rotation 0 0 1 0.5236003061004253 + name "wall(13)" + size 0.05 0.9 0.2 +} +Wall { + translation -1.9 1.3 0 + rotation 0 0 1 0.5236003061004253 + name "wall(14)" + size 0.05 1.2 0.2 +} +Wall { + translation 2.2 3.3 0 + rotation 0 0 1 -2.7488996938995744 + name "wall(3)" + size 0.05 2 0.2 +} +Wall { + translation -0.5 4.2 0 + rotation 0 0 1 -1.5707996938995747 + name "wall(1)" + size 0.05 4.5 0.2 +} From a1fc3cb59fb7c682f43a1110b7d25e8944caec1f Mon Sep 17 00:00:00 2001 From: matthiasbienvenu Date: Fri, 22 Nov 2024 18:13:26 +0100 Subject: [PATCH 2/9] added supervisor, new pistes, vehicules ... --- .../controllerVehiculeDriver.py | 209 -- .../controllerWorldInitSupervisor.py | 0 ...tfevents.1664312862.Chrysanthe-TOSH.2080.0 | Bin ...tfevents.1664356896.Chrysanthe-TOSH.6960.0 | Bin ...tfevents.1664442064.Chrysanthe-TOSH.5336.0 | Bin ...tfevents.1664793148.Chrysanthe-TOSH.6404.0 | Bin ...tfevents.1664793499.Chrysanthe-TOSH.6312.0 | Bin .../controllerVehicleDriver.py | 77 + .../controllerWorldSupervisor.py | 180 ++ .../controller_jaune/controller_jaune.py | 112 + .../controller_violet/controller_violet.py | 112 + Simulateur/protos/.~lock.TT02.proto# | 1 + Simulateur/protos/Carrosserie_Audi_Light.stl | Bin 0 -> 897884 bytes Simulateur/protos/Carrosserie_BMW_light.stl | Bin 0 -> 857184 bytes Simulateur/protos/ChevroletCamaroLight.stl | Bin 0 -> 123184 bytes Simulateur/protos/RpLidarA2.proto | 86 + Simulateur/protos/TT02Wheel.proto | 32 + Simulateur/protos/TT02_2023b.proto | 279 ++ Simulateur/protos/car.proto | 894 ------- Simulateur/protos/icons/Altino.png | Bin 0 -> 14360 bytes Simulateur/protos/icons/Car.png | Bin 0 -> 9140 bytes Simulateur/protos/icons/RpLidarA2.png | Bin 0 -> 11368 bytes .../worlds/{.Piste.wbproj => .piste.wbproj} | 5 +- Simulateur/worlds/.piste1.wbproj | 9 + Simulateur/worlds/.piste2.wbproj | 9 + .../{car.wbo => deprecated_vehicle.wbt} | 2 + Simulateur/worlds/{piste.wbt => piste1.wbt} | 20 +- Simulateur/worlds/piste2.wbt | 2234 +++++++++++++++++ 28 files changed, 3154 insertions(+), 1107 deletions(-) delete mode 100644 Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py delete mode 100644 Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py rename Simulateur/controllers/{VehiculeDriver => controllerVehicleDriver}/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 (100%) rename Simulateur/controllers/{VehiculeDriver => controllerVehicleDriver}/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 (100%) rename Simulateur/controllers/{VehiculeDriver => controllerVehicleDriver}/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 (100%) rename Simulateur/controllers/{VehiculeDriver => controllerVehicleDriver}/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 (100%) rename Simulateur/controllers/{VehiculeDriver => controllerVehicleDriver}/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 (100%) create mode 100644 Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py create mode 100644 Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py create mode 100644 Simulateur/controllers/controller_jaune/controller_jaune.py create mode 100644 Simulateur/controllers/controller_violet/controller_violet.py create mode 100644 Simulateur/protos/.~lock.TT02.proto# create mode 100644 Simulateur/protos/Carrosserie_Audi_Light.stl create mode 100644 Simulateur/protos/Carrosserie_BMW_light.stl create mode 100644 Simulateur/protos/ChevroletCamaroLight.stl create mode 100644 Simulateur/protos/RpLidarA2.proto create mode 100644 Simulateur/protos/TT02Wheel.proto create mode 100644 Simulateur/protos/TT02_2023b.proto delete mode 100644 Simulateur/protos/car.proto create mode 100644 Simulateur/protos/icons/Altino.png create mode 100644 Simulateur/protos/icons/Car.png create mode 100644 Simulateur/protos/icons/RpLidarA2.png rename Simulateur/worlds/{.Piste.wbproj => .piste.wbproj} (59%) create mode 100644 Simulateur/worlds/.piste1.wbproj create mode 100644 Simulateur/worlds/.piste2.wbproj rename Simulateur/worlds/{car.wbo => deprecated_vehicle.wbt} (99%) rename Simulateur/worlds/{piste.wbt => piste1.wbt} (80%) create mode 100644 Simulateur/worlds/piste2.wbt diff --git a/Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py b/Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py deleted file mode 100644 index 127fd48..0000000 --- a/Simulateur/controllers/VehiculeDriver/controllerVehiculeDriver.py +++ /dev/null @@ -1,209 +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, vehicle): - #Initialisation du driver - self.driver = Driver() - - basicTimeStep = int(self.driver.getBasicTimeStep()) - self.sensorTime = basicTimeStep // 4 - - #Paramètre de la voiture (position, etc..) - self.vehicle = vehicle #self.driver.getFromDef("vehicle") - print(self.vehicle) - print(self.driver.__dict__) - #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.vehicle.getField("translation") - self.rot_champs = self.vehicle.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 - - - -def create_vehicles(n): - """ - copies the default DEF vehicule Robot n_envs times so that they can be controlled independently - """ - supervisor = Supervisor() - node = supervisor.getFromDef("vehicle") - children_field = node.getParentNode().getField("children") - print(children_field.__dict__) - vehicles = [] - - for i in range(n): - # create a copy of the old node but with a new name - index = children_field.importMFNodeFromString(-1, node.exportString()) - node = children_field.getMFNode(index) - vehicles.append(node) - return vehicles - - -#----------------Programme principal-------------------- -def main(): - n = 1 # number of vehicles - #vehicles = create_vehicles(n) - # env = SubprocVecEnv([lambda: WebotsGymEnvironment(i) for i in range(n_envs)]) - vehicles = [Supervisor().getFromDef("vehicle")] - env = WebotsGymEnvironment(vehicles[0]) - 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="cuda:0" - ) - - #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/Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py b/Simulateur/controllers/WorldInitSupervisor/controllerWorldInitSupervisor.py deleted file mode 100644 index e69de29..0000000 diff --git a/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 b/Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 similarity index 100% rename from Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 rename to Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_1/events.out.tfevents.1664312862.Chrysanthe-TOSH.2080.0 diff --git a/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 b/Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 similarity index 100% rename from Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 rename to Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_2/events.out.tfevents.1664356896.Chrysanthe-TOSH.6960.0 diff --git a/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 b/Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 similarity index 100% rename from Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 rename to Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_1/events.out.tfevents.1664442064.Chrysanthe-TOSH.5336.0 diff --git a/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 b/Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 similarity index 100% rename from Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 rename to Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_2/events.out.tfevents.1664793148.Chrysanthe-TOSH.6404.0 diff --git a/Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 b/Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 similarity index 100% rename from Simulateur/controllers/VehiculeDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 rename to Simulateur/controllers/controllerVehicleDriver/Webots_tb/PPO_voiture_webots_aleatoire_3/events.out.tfevents.1664793499.Chrysanthe-TOSH.6312.0 diff --git a/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py b/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py new file mode 100644 index 0000000..a61c36d --- /dev/null +++ b/Simulateur/controllers/controllerVehicleDriver/controllerVehicleDriver.py @@ -0,0 +1,77 @@ +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 Robot + + +class VehicleDriver(Driver, Robot): + """ + 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.car = self.getFromDef(self.getName()) + + #Lidar + self.lidar = self.getDevice("RpLidarA2") + self.lidar.enable(self.sensorTime) + self.lidar.enablePointCloud() + + #Capteur de distance + self.front_center_sensor = self.getDevice("front_center_sensor") + self.side_left_sensor = self.getDevice("side_left_sensor") + self.side_right_sensor = self.getDevice("side_right_sensor") + self.front_center_sensor.enable(self.sensorTime) + self.side_left_sensor.enable(self.sensorTime) + self.side_right_sensor.enable(self.sensorTime) + + #Capteur de balise + self.up_sensor = self.getDevice("up_sensor") + self.up_sensor.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.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 + + #Fonction step de l"environnement GYM + def step(self): + steeringAngle = self.car.getField("steeringAngleInstruction").getSFFloat() + self.setSteeringAngle(steeringAngle) + self.setCruisingSpeed(100) + + return super().step() + + def run(self): + while self.step() != -1: + pass + + +#----------------Programme principal-------------------- +def main(): + driver = VehicleDriver() + driver.run() + + +if __name__ == "__main__": + main() diff --git a/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py b/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py new file mode 100644 index 0000000..20ad8b8 --- /dev/null +++ b/Simulateur/controllers/controllerWorldSupervisor/controllerWorldSupervisor.py @@ -0,0 +1,180 @@ +from typing import * +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 controller import Supervisor + + +class WebotsGymEnvironment(gym.Env): + + def __init__(self, n: int, supervisor: Supervisor): + self.supervisor = supervisor + self.n = n + self.lidar_horizontal_resolution = 512 + + basicTimeStep = int(self.supervisor.getBasicTimeStep()) + self.sensorTime = basicTimeStep // 4 + + #Paramètre de la voiture (position, etc..) + self.vehicles = [] + self.create_vehicles() + + self.action_space = gym.spaces.Discrete(5) #actions disponibles + min = np.zeros(self.lidar_horizontal_resolution) + max = np.ones(self.lidar_horizontal_resolution) + self.observation_space = gym.spaces.Box(min, max, dtype=np.float32) #Etat venant du LIDAR + print(self.observation_space) + print(self.observation_space.shape) + + self.translation_fields = [self.vehicles[i].getField("translation") for i in range(n)] + self.rotation_fields = [self.vehicles[i].getField("rotation") for i in range(n)] # idk why but if this goes before the lidar it will not work + + + def create_vehicles(self): + """ + copies the default DEF vehicle Robot n_envs times so that they can be controlled independently + """ + # create a group of n vehicles + robot = self.supervisor.getFromDef("WorldSupervisor") + children_field = robot.getField("children") + for i in range(self.n): + # looks bad but using triple quotes is even worse + proto_string = \ + f'DEF TT02_{i} TT02_2023b' '{' \ + f' translation -1 -2.5 0.04' \ + f' name "TT02_{i}"' \ + f' controller "controllerVehicleDriver"' \ + f' color 0.5 0 0.6' \ + f' lidarHorizontalResolution {self.lidar_horizontal_resolution}' \ + f' steeringAngleInstruction 0' \ + f' lidarData [{self.lidar_horizontal_resolution * ' 0. '}]' \ + '}' + + print("Spawning vehicle:") + print(proto_string) + + index = children_field.importMFNodeFromString(-1, proto_string) + vehicle = children_field.getMFNode(index) + self.vehicles.append(vehicle) + + # returns the lidar data of all vehicles + def observe(self): + res = np.array([ + [ + v.getField("lidarData").getMFFloat(j) + for j in range(self.lidar_horizontal_resolution) + ] + for v in self.vehicles + ], dtype=np.float32) + return res + + # reset the gym environment + def reset(self, seed=0): + #Valeur aléatoire + # car width: 0.25 + for i in range(self.n): + y = -3.3 + i * 0.25 + INITIAL_trans = [-1, y, 0.0399538] + INITIAL_rot = [-0.304369, -0.952554, -8.76035e-05 , 6.97858e-06] + self.translation_fields[i].setSFVec3f(INITIAL_trans) + self.rotation_fields[i].setSFRotation(INITIAL_rot) + + time.sleep(0.3) #Temps de pause après réinitilialisation + + obs = self.observe() + print(obs.shape) + #super().step() + info = {} + return obs, info + + # step function of the gym environment + def step(self, action): + for vehicle in self.vehicles: + vehicle.getField("steeringAngleInstruction").setSFFloat([-.3, -.1, 0, .1, .3][action]) + # we should add a beacon sensor pointing upwards to detect the beacon + + obs = self.observe() + + reward = 0 + done = False + truncated = False + + front, left, right, up = [self.vehicles[0].getField("distanceSensorData").getMFFloat(i) for i in range(4)] + + if front >= 900 and not(done): + print("Collision avant") + reward = -100 + done = True + elif ((front >= 854 and left >= 896) or (front >= 696 and left >= 910) or left >= 937) and not(done): + print("Collision gauche") + reward = -100 + done = True + elif ((front >= 850 and right >= 893) or (front >= 584 and right >= 910) or right >= 961) and not(done): + print("Collision droite") + reward = -100 + done = True + elif up > 700: + done = False + print("Balise passée") + reward = 20 + else: + done = False + reward = 0 + + self.supervisor.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 = 1 # number of vehicles + env = WebotsGymEnvironment(n) + 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="cuda:0" + ) + + #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/Simulateur/controllers/controller_jaune/controller_jaune.py b/Simulateur/controllers/controller_jaune/controller_jaune.py new file mode 100644 index 0000000..def8b94 --- /dev/null +++ b/Simulateur/controllers/controller_jaune/controller_jaune.py @@ -0,0 +1,112 @@ +# Copyright 1996-2022 Cyberbotics Ltd. +# +# Controle de la voiture TT-02 simulateur CoVAPSy pour Webots 2023b +# Inspiré de vehicle_driver_altino controller +# Kévin Hoarau, Anthony Juton, Bastien Lhopitallier, Martin Raynaud +# août 2023 + +from vehicle import Driver +from controller import Lidar +import numpy as np +import time + +driver = Driver() + +basicTimeStep = int(driver.getBasicTimeStep()) +sensorTimeStep = 4 * basicTimeStep + +#Lidar +lidar = Lidar("RpLidarA2") +lidar.enable(sensorTimeStep) +lidar.enablePointCloud() + +#clavier +keyboard = driver.getKeyboard() +keyboard.enable(sensorTimeStep) + +# vitesse en km/h +speed = 0 +maxSpeed = 28 #km/h + +# angle de la direction +angle = 0 +maxangle_degre = 16 + + +# mise a zéro de la vitesse et de la direction +driver.setSteeringAngle(angle) +driver.setCruisingSpeed(speed) + +tableau_lidar_mm=[0]*360 + +def set_vitesse_m_s(vitesse_m_s): + speed = vitesse_m_s*3.6 + if speed > maxSpeed : + speed = maxSpeed + if speed < 0 : + speed = 0 + driver.setCruisingSpeed(speed) + +def set_direction_degre(angle_degre): + if angle_degre > maxangle_degre: + angle_degre = maxangle_degre + elif angle_degre < -maxangle_degre: + angle_degre = -maxangle_degre + angle = -angle_degre * 3.14/180 + driver.setSteeringAngle(angle) + +def recule(): #sur la voiture réelle, il y a un stop puis un recul pendant 1s. + driver.setCruisingSpeed(-1) + +# mode auto desactive +modeAuto = False +print("cliquer sur la vue 3D pour commencer") +print("a pour mode auto (pas de mode manuel sur TT02_jaune), n pour stop") + +while driver.step() != -1: + while True: + #acquisition des donnees du lidar + # recuperation de la touche clavier + currentKey = keyboard.getKey() + + if currentKey == -1: + break + + elif currentKey == ord('n') or currentKey == ord('N'): + if modeAuto : + modeAuto = False + print("--------Modes Auto TT-02 jaune Désactivé-------") + elif currentKey == ord('a') or currentKey == ord('A'): + if not modeAuto : + modeAuto = True + print("------------Mode Auto TT-02 jaune Activé-----------------") + + #acquisition des donnees du lidar + donnees_lidar_brutes = lidar.getRangeImage() + for i in range(360) : + if (donnees_lidar_brutes[-i]>0) and (donnees_lidar_brutes[-i]<20) : + tableau_lidar_mm[i] = 1000*donnees_lidar_brutes[-i] + else : + tableau_lidar_mm[i] = 0 + + if not modeAuto: + set_direction_degre(0) + set_vitesse_m_s(0) + + if modeAuto: + ######################################################## + # Programme etudiant avec + # - le tableau tableau_lidar_mm + # - la fonction set_direction_degre(...) + # - la fonction set_vitesse_m_s(...) + # - la fonction recule() + ####################################################### + + #un secteur par tranche de 20° donc 10 secteurs numérotés de 0 à 9 + angle_degre = 0.02*(tableau_lidar_mm[60]-tableau_lidar_mm[-60]) + set_direction_degre(angle_degre) + vitesse_m_s = 0.5 + set_vitesse_m_s(vitesse_m_s) + + ######################################################### + diff --git a/Simulateur/controllers/controller_violet/controller_violet.py b/Simulateur/controllers/controller_violet/controller_violet.py new file mode 100644 index 0000000..0a2ab39 --- /dev/null +++ b/Simulateur/controllers/controller_violet/controller_violet.py @@ -0,0 +1,112 @@ +# Copyright 1996-2022 Cyberbotics Ltd. +# +# Controle de la voiture TT-02 simulateur CoVAPSy pour Webots 2023b +# Inspiré de vehicle_driver_altino controller +# Kévin Hoarau, Anthony Juton, Bastien Lhopitallier, Martin Taynaud +# juillet 2023 + + +from vehicle import Driver +from controller import Lidar + +driver = Driver() + +basicTimeStep = int(driver.getBasicTimeStep()) +sensorTimeStep = 4 * basicTimeStep + +#Lidar +lidar = Lidar("RpLidarA2") +lidar.enable(sensorTimeStep) +lidar.enablePointCloud() + +#clavier +keyboard = driver.getKeyboard() +keyboard.enable(sensorTimeStep) + +# vitesse en km/h +speed = 0 +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... + +# mise a zéro de la vitesse et de la direction +driver.setSteeringAngle(angle) +driver.setCruisingSpeed(speed) +# mode manuel et mode auto desactive +modeManuel = False +modeAuto = False +print("cliquer sur la vue 3D pour commencer") +print("m pour mode manuel, a pour mode auto, n pour stop, l pour affichage données lidar") +print("en mode manuel utiliser les flèches pour accélérer, freiner et diriger") + +while driver.step() != -1: + + speed = driver.getTargetCruisingSpeed() + + while True: + #acquisition des donnees du lidar + donnees_lidar = lidar.getRangeImage() + + # recuperation de la touche clavier + currentKey = keyboard.getKey() + if currentKey == -1: + break + if currentKey == ord('m') or currentKey == ord('M'): + if not modeManuel: + modeManuel = True + modeAuto = False + print("------------Mode Manuel Activé---------------") + elif currentKey == ord('n') or currentKey == ord('N'): + if modeManuel or modeAuto : + modeManuel = False + modeAuto = False + print("--------Modes Manuel et Auto Désactivé-------") + elif currentKey == ord('a') or currentKey == ord('A'): + if not modeAuto : + modeAuto = True + modeManuel = False + print("------------Mode Auto Activé-----------------") + elif currentKey == ord('l') or currentKey == ord('L'): + print("-----donnees du lidar en metres sens horaire au pas de 1°-----") + for i in range(len(donnees_lidar)) : + print(f"{donnees_lidar[i]:.3f} ", end='') + if (i+1)%10 == 0 : + print() + print() + + # Controle en mode manuel + if modeManuel: + if currentKey == keyboard.UP: + speed += 0.2 + elif currentKey == keyboard.DOWN: + speed -= 0.2 + elif currentKey == keyboard.LEFT: + angle -= 0.04 + elif currentKey == keyboard.RIGHT: + angle += 0.04 + + if not modeManuel and not modeAuto: + speed = 0 + angle = 0 + + if modeAuto: + speed = 3 #km/h + #l'angle de la direction est la différence entre les mesures des rayons + #du lidar à (-99+18*2)=-63° et (-99+81*2)=63° + angle = donnees_lidar[60]-donnees_lidar[300] + + # clamp speed and angle to max values + if speed > maxSpeed: + speed = maxSpeed + elif speed < -1 * maxSpeed: + speed = -1 * maxSpeed + if angle > maxangle: + angle = maxangle + elif angle < -maxangle: + angle = -maxangle + + driver.setCruisingSpeed(speed) + driver.setSteeringAngle(angle) + diff --git a/Simulateur/protos/.~lock.TT02.proto# b/Simulateur/protos/.~lock.TT02.proto# new file mode 100644 index 0000000..5f6d48f --- /dev/null +++ b/Simulateur/protos/.~lock.TT02.proto# @@ -0,0 +1 @@ +,ajuton,juton-Dell-5410,22.07.2023 10:06,file:///home/ajuton/.config/libreoffice/4; \ No newline 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