class P9RLEnv(gym.Env):
metadata = {'render.modes': ['human']}
motors = []
timeStep = 1
restrictedGoals = True
boxEnv = False
isFixedGoal = False
fixedGoal = [3.5, 0]
logging = False
maxTimestep = 7000
robotResetLocation = [0, 0, 0]
def __init__(self):
# Initialize TurtleBot specifics
self.maxAngSpeed = 1.5 # 2.84 max
self.maxLinSpeed = 0.1 # 0.22 max
# Initialize reward parameters:
self.moveTowardsParam = 1
self.moveAwayParam = 1.1
self.safetyLimit = 0.75
self.obsProximityParam = 1
self.EOEPunish = -300
self.EOEReward = 600
self.EOERewardCounter = 0
self.EpisodeCounter = 0
# Total reward counter for each component
self.moveTowardGoalTotalReward = 0
self.obsProximityTotalPunish = 0
# Initialize RL specific variables
self.rewardInterval = 1 # How often do you want to get rewards
self.epConc = '' # Conculsion of episode [Collision, Success, TimeOut]
self.reward = 0 # Reward gained in the timestep
self.totalreward = 0 # Reward gained throughout the episode
self.counter = 0 # Timestep counter
self.needReset = True #
self.state = [] # State of the environment
self.done = False # If the episode is done
self.action = [0, 0] # Current action chosen
self.prevAction = [0, 0] # Past action chosen
self.goal = [] # Goal
self.goals = [x / 100 for x in range(-450, 451, 150)]
#self.goals = [[0.72, 6.78],[5.03, 6.78],[12.7, 6.78], [10.9, -1.68], [3, 0],[-11.5, -2.5]]
self.seeded = False
self.dist = 0 # Distance to the goal
self.prevDist = 0 # Previous distance to the goal
# Logging variables
self.currentEpisode = 0 # Current episode number
self.start = 0 # Timer for starting
self.duration = 0 # Episode duration in seconds
self.epInfo = [] # Logging info for the episode
self.startPosition = []
self.prevMax = 0 # temporary variable
# Initialize a supervisor
self.supervisor = Supervisor()
# Initialize robot
self.robot = self.supervisor.getFromDef('TB')
self.translationField = self.robot.getField('translation')
self.orientationField = self.robot.getField('rotation')
# Initialize lidar
self.lidar = self.supervisor.getLidar('LDS-01')
self.lidarDiscretization = 30
self.lidar.enable(self.timeStep)
self.lidarRanges = []
# Initialize Motors
self.motors.append(self.supervisor.getMotor("right wheel motor"))
self.motors.append(self.supervisor.getMotor("left wheel motor"))
self.motors[0].setPosition(float("inf"))
self.motors[1].setPosition(float("inf"))
self.motors[0].setVelocity(0.0)
self.motors[1].setVelocity(0.0)
self.direction = []
self.position = []
self.orientation = []
# Initialize Goal Object
self.goalObject = self.supervisor.getFromDef('GOAL')
self.goalTranslationField = self.goalObject.getField('translation')
# Initialize action-space and observation-space
self.action_space = spaces.Box(low=np.array(
[-self.maxLinSpeed, -self.maxAngSpeed]),
high=np.array([0, self.maxAngSpeed]),
dtype=np.float32)
#TODO: MAKE POSTIVE LINEAR SPEED A FORWARD MOTION
self.observation_space = spaces.Box(low=-10,
high=10,
shape=(14, ),
dtype=np.float16)
self.counter = 0
def reset(self):
self._startEpisode() # Reset evetything needs resetting
self.EpisodeCounter = self.EpisodeCounter + 1
print(self.EpisodeCounter)
if not self.seeded:
random.seed(self.currentEpisode)
self.seeded = True
# Make it Happen
self.supervisor.step(self.timeStep)
self.goal = self._setGoal() # Set Goal
self._resetObject(self.goalObject, [self.goal[0], 0.01, self.goal[1]])
self._getState()
self.startPosition = self.position[:]
self.state = [self.dist, self.direction
] + self.lidarRanges # Set State
#self.state = [self.dist, self.direction] + self.lidarRanges # Set State
return np.asarray(self.state)
def step(self, action):
self.action = action # Take action
self._take_action(action)
self.counter += 1
#sself.supervisor.simulationSetMode(3)
self._getState() # Observe new state
self.state = np.asarray([self.dist, self.direction] + self.lidarRanges)
self.prevAction = self.action[:] # Set previous action
# Get State(dist from obstacle + lidar) and convert to numpyarray
#print(self.lidarRanges[0])
self.supervisor.step(32)
self.reward = self._calculateReward() # get Reward
self.done, extraReward = self._isDone(
) # determine if state is Done, extra reward/punishment
self.reward += extraReward
self.totalreward += self.reward
return [self.state, self.reward, self.done, {}]
def _trimLidarReadings(self, lidar):
lidarReadings = []
new_lidars = [x if x != 0 else 3.5
for x in lidar] # Replace 0's with 3.5 (max reading)
for x in range(0, 360, self.lidarDiscretization):
end = x + self.lidarDiscretization
lidarReadings.append(min(
new_lidars[x:end])) # Take the minimum of lidar Readings
return lidarReadings
def _resetObject(self, object, coordinates):
object.getField('translation').setSFVec3f(coordinates)
object.getField('rotation').setSFRotation([0, 1, 0, 0])
object.resetPhysics()
def _setGoal(self):
if len(self.goals) == 6: return random.choice(self.goals)
if not self.isFixedGoal:
while True:
if self.restrictedGoals:
gs = random.sample(self.goals, 1)
gs.append(random.randrange(-45, 45) / 10)
g = gs[:] if np.random.randint(2) == 0 else [gs[1], gs[0]]
elif self.boxEnv:
xGoal = random.randrange(
-40, -25) / 10 if np.random.randint(
2) == 0 else random.randrange(25, 40) / 10
yGoal = random.randrange(-40, 40) / 10
g = [yGoal, xGoal
] if np.random.randint(2) == 0 else [xGoal, yGoal]
else:
xGoal = random.randrange(-40, 40) / 10
yGoal = random.randrange(-40, 40) / 10
g = [xGoal, yGoal]
pos1 = self.robot.getPosition()[0]
pos2 = self.robot.getPosition()[2]
distFromGoal = ((g[0] - pos1)**2 + (g[1] - pos2)**2)**0.5
if distFromGoal >= 1:
break
else:
g = self.fixedGoal
print('Goal set with Coordinates < {}, {} >'.format(g[0], g[1]))
return g
def _getDist(self):
return ((self.goal[0] - self.position[0])**2 +
(self.goal[1] - self.position[1])**2)**0.5
def _take_action(self, action):
if self.logging:
print(
'\t\033[1mLinear velocity:\t{}\n\tAngular velocity:\t{}\n\033[0m'
.format(action[0], action[1]))
convertedVelocity = self.setVelocities(action[0], action[1])
self.motors[0].setVelocity(float(convertedVelocity[0]))
self.motors[1].setVelocity(float(convertedVelocity[1]))
def _isDone(self):
if self.counter >= self.maxTimestep: # Check maximum timestep
self.needReset = True
return True, self.EOEPunish
minScan = min(list(filter(lambda a: a != 0,
self.lidarRanges[:]))) # Check LiDar
if minScan < 0.2:
self.needReset = True
return True, self.EOEPunish
elif self.dist < 0.35: # Check Goal
self.needReset = True if self.boxEnv else False
self.EOERewardCounter = self.EOERewardCounter + 1
print("SUCCESS COUNTER:", self.EOERewardCounter)
return True, self.EOEReward
else:
return False, 0
def render(self, mode='human', close=False):
pass
def setVelocities(self, linearV, angularV):
R = 0.033 # Wheel radius
L = 0.138 # Wheelbase length
# vr = (((2 * linearV) + (angularV * L)) / (2 * R))
vr = ((linearV + (L / 2) * angularV)) / R
vl = ((linearV - (L / 2) * angularV)) / R
return [vl, vr]
def _getDirection(self):
# Get direction of goal from Robot
robgoaly = self.goal[1] - self.position[1]
robgoalx = self.goal[0] - self.position[0]
goal_angle = math.atan2(robgoalx, robgoaly)
# Get difference between goal direction and orientation
heading = goal_angle - self.orientation
if heading > pi: # Wrap around pi
heading -= 2 * pi
elif heading < -pi:
heading += 2 * pi
return heading
def _calculateReward(self):
action1 = np.round(self.action[0], 1)
action2 = np.round(self.action[1], 1)
if self.counter % self.rewardInterval != 0:
return 0 # Check if it's Reward Time
distRate = self.prevDist - self.dist
if self.prevDist == 0:
self.prevDist = self.dist
return 0
moveTowardsGoalReward = self._rewardMoveTowardsGoal(distRate)
self.moveTowardGoalTotalReward += moveTowardsGoalReward
obsProximityPunish = self._rewardObstacleProximity()
self.obsProximityTotalPunish += obsProximityPunish
reward = moveTowardsGoalReward + obsProximityPunish * 3
totalRewardDic = {
"faceObstacleTotalPunish": self.faceObstacleTotalPunish,
"obsProximityTotalPunish": self.obsProximityTotalPunish,
"moveTowardGoalTotalReward": self.moveTowardGoalTotalReward
}
if self.logging:
self._printRewards(totalRewardDic)
print(reward)
self.prevDist = self.dist
return reward
def _rewardMoveTowardsGoal(self, distRate):
if distRate <= 0:
return self.moveAwayParam * (distRate / 0.0044)
else:
return self.moveTowardsParam * (distRate / 0.0044)
def _rewardObstacleProximity(self):
closestObstacle = min(self.lidarRanges)
if closestObstacle <= self.safetyLimit:
return self.obsProximityParam * -(
1 - (closestObstacle / self.safetyLimit))
else:
return 0
def _startEpisode(self):
if self.needReset: # Reset object(s) / counters / rewards
print('Resetting Robot...')
self._resetObject(self.robot, self.robotResetLocation)
self.counter = 0
self.totalreward = 0
self.moveTowardGoalTotalReward = 0
self.obsProximityTotalPunish = 0
self.faceObstacleTotalPunish = 0
self.prevDist = 0
def _getState(self):
self.lidarRanges = self._trimLidarReadings(
self.lidar.getRangeImage()) # Get LidarInfo
self.position = self.robot.getPosition(
)[::2] # Get Position (x,y) and orientation
self.orientation = self.orientationField.getSFRotation()[3]
self.dist = self._getDist() # Get Eucledian distance from the goal
self.direction = self._getDirection()
def _printRewards(self, r, interval=1):
if self.counter % interval == 0:
print("\n\033[1mEpisode {}, timestep {}\033[0m".format(
self.currentEpisode, self.counter))
for k, v in r.items():
print("\t" + str(k) + ":\t" + str(v))
def _printMax(self, reward):
maxR = reward if self.prevMax < reward else self.prevMax
self.prevMax = maxR
print("max: {}".format(maxR))
def _printLidarRanges(self):
lidarFormatted = []
for i in range(len(self.lidarRanges)):
lidarFormatted.append(str(i) + ': ' + str(self.lidarRanges[i]))
print(lidarFormatted)
def _logEpisode(self):
self.duration = time.time() - self.start
self.epInfo.append([
round(self.duration, 3),
round(self.totalreward, 3), self.epConc, self.counter, "Goal:",
self.goal, "Start position:", self.startPosition, "Rewards:",
self.moveTowardGoalTotalReward, self.obsProximityTotalPunish,
self.faceObstacleTotalPunish
])
class P7RLEnv(gym.Env):
metadata = {'render.modes': ['human']}
motors = []
timeStep = 32
restrictedGoals = True
boxEnv = False
isFixedGoal = False
fixedGoal = [3.5, 0]
logging = True
maxTimestep = 20000
robotResetLocation = [0, 0, 0]
def __init__(self):
# Initialize TurtleBot specifics
self.maxAngSpeed = 1 # 2.84 max
self.maxLinSpeed = 0.14 # 0.22 max
# Initialize reward parameters:
self.moveTowardsParam = 1
self.moveAwayParam = 1.1
self.safetyLimit = 0.5
self.obsProximityParam = 0.01
self.faceObstacleParam = 10
# Total reward counter for each component
self.moveTowardGoalTotalReward = 0
self.obsProximityTotalPunish = 0
self.faceObstacleTotalPunish = 0
# Initialize RL specific variables
self.rewardInterval = 1 # How often do you want to get rewards
self.epConc = '' # Conculsion of episode [Collision, Success, TimeOut]
self.reward = 0 # Reward gained in the timestep
self.totalreward = 0 # Reward gained throughout the episode
self.counter = 0 # Timestep counter
self.needReset = True #
self.state = [] # State of the environment
self.done = False # If the episode is done
self.action = [0, 0] # Current action chosen
self.prevAction = [0, 0] # Past action chosen
self.goal = [] # Goal
self.goals = [x / 100 for x in range(-450, 451, 150)]
self.dist = 0 # Distance to the goal
self.prevDist = 0 # Previous distance to the goal
# Logging variables
self.logDir = ""
self.path = ""
self.currentEpisode = 0 # Current episode number
self.start = 0 # Timer for starting
self.duration = 0 # Episode duration in seconds
self.epInfo = [] # Logging info for the episode
self.startPosition = []
self.prevMax = 0 # temporary variable
# Initialize a supervisor
self.supervisor = Supervisor()
# Initialize robot
self.robot = self.supervisor.getFromDef('TB')
self.translationField = self.robot.getField('translation')
self.orientationField = self.robot.getField('rotation')
# Initialize lidar
self.lidar = self.supervisor.getLidar('TurtleBotLidar')
self.lidar.enable(self.timeStep)
self.lidarRanges = []
# Initialize Motors
self.motors.append(self.supervisor.getMotor("left wheel motor"))
self.motors.append(self.supervisor.getMotor("right wheel motor"))
self.motors[0].setPosition(float("inf"))
self.motors[1].setPosition(float("inf"))
self.motors[0].setVelocity(0.0)
self.motors[1].setVelocity(0.0)
self.direction = []
self.position = []
self.orientation = []
# Initialize Goal Object
self.goalObject = self.supervisor.getFromDef('GOAL')
self.goalTranslationField = self.goalObject.getField('translation')
# Initialize action-space and observation-space
self.action_space = spaces.Box(
low=np.array([-self.maxLinSpeed, -self.maxAngSpeed]),
high=np.array([self.maxLinSpeed, self.maxAngSpeed]),
dtype=np.float32)
self.observation_space = spaces.Box(low=-10,
high=10,
shape=(24, ),
dtype=np.float16)
def reset(self):
if not self.logDir: self._getLogDirectory()
self._startEpisode() # Reset evetything needs resetting
self.supervisor.step(1) # Make it Happen
self.goal = self._setGoal() # Set Goal
self._resetObject(self.goalObject, [self.goal[0], 0.01, self.goal[1]])
self._getState()
self.startPosition = self.position[:]
self.state = [
self.prevAction[0], self.prevAction[1], self.dist, self.direction
] + self.lidarRanges # Set State
return np.asarray(self.state)
def step(self, action):
self.prevAction = self.action[:] # Set previous action
self.action = action # Take action
self._take_action(action)
self.supervisor.step(1)
self.counter += 1
# Get State(dist from obstacle + lidar) and convert to numpyarray
self._getState() # Observe new state
self.state = np.asarray([
self.prevAction[0], self.prevAction[1], self.dist, self.direction
] + self.lidarRanges)
self.reward = self._calculateReward() # get Reward
self.done, extraReward = self._isDone(
) # determine if state is Done, extra reward/punishment
self.reward += extraReward
self.totalreward += self.reward
return [self.state, self.reward, self.done, {}]
def _trimLidarReadings(self, lidar):
lidarReadings = []
new_lidars = [x if x != 0 else 3.5
for x in lidar] # Replace 0's with 3.5 (max reading)
for x in range(0, 360, 18):
end = x + 18
lidarReadings.append(min(
new_lidars[x:end])) # Take the minimum of lidar Readings
return lidarReadings
def _resetObject(self, object, coordinates):
object.getField('translation').setSFVec3f(coordinates)
object.getField('rotation').setSFRotation([0, 1, 0, 0])
object.resetPhysics()
def _setGoal(self):
if not self.isFixedGoal:
while True:
if self.restrictedGoals:
gs = random.sample(self.goals, 1)
gs.append(random.randrange(-45, 45) / 10)
g = gs[:] if np.random.randint(2) == 0 else [gs[1], gs[0]]
elif self.boxEnv:
xGoal = random.randrange(
-40, -25) / 10 if np.random.randint(
2) == 0 else random.randrange(25, 40) / 10
yGoal = random.randrange(-40, 40) / 10
g = [yGoal, xGoal
] if np.random.randint(2) == 0 else [xGoal, yGoal]
else:
xGoal = random.randrange(-40, 40) / 10
yGoal = random.randrange(-40, 40) / 10
g = [xGoal, yGoal]
pos1 = self.robot.getPosition()[0]
pos2 = self.robot.getPosition()[2]
distFromGoal = ((g[0] - pos1)**2 + (g[1] - pos2)**2)**0.5
if distFromGoal >= 1:
break
else:
g = self.fixedGoal
print('Goal set with Coordinates < {}, {} >'.format(g[0], g[1]))
return g
def _getDist(self):
return ((self.goal[0] - self.position[0])**2 +
(self.goal[1] - self.position[1])**2)**0.5
def _take_action(self, action):
if self.logging:
print(
'\t\033[1mLinear velocity:\t{}\n\tAngular velocity:\t{}\n\033[0m'
.format(action[0], action[1]))
convertedVelocity = self.setVelocities(action[0], action[1])
self.motors[0].setVelocity(float(convertedVelocity[0]))
self.motors[1].setVelocity(float(convertedVelocity[1]))
def _isDone(self):
if self.counter >= self.maxTimestep: # Check maximum timestep
self.needReset = True
self._endEpisode('timeout')
return True, 0
minScan = min(list(filter(lambda a: a != 0,
self.lidarRanges[:]))) # Check LiDar
if minScan < 0.25:
self.needReset = True
self._endEpisode('collision')
return True, -500
elif self.dist < 0.35: # Check Goal
self.needReset = True if self.boxEnv else False
self._endEpisode('success')
return True, 500
else:
return False, 0
def render(self, mode='human', close=False):
pass
def setVelocities(self, linearV, angularV):
R = 0.033 # Wheel radius
L = 0.138 # Wheelbase length
vr = (2 * linearV + angularV * L) / (2 * R)
vl = (2 * linearV - angularV * L) / (2 * R)
#print('\nCommanded linear:\t{} angular:\t {}'.format(linearV, angularV))
#print('Calculated right wheel:\t{}, left wheel:\t {}'.format(vr, vl))
return [vl, vr]
def _getDirection(self):
# Get direction of goal from Robot
robgoaly = self.goal[1] - self.position[1]
robgoalx = self.goal[0] - self.position[0]
goal_angle = math.atan2(robgoalx, robgoaly)
# Get difference between goal direction and orientation
heading = goal_angle - self.orientation
if heading > pi: # Wrap around pi
heading -= 2 * pi
elif heading < -pi:
heading += 2 * pi
return heading
def _calculateReward(self):
action1 = np.round(self.action[0], 1)
action2 = np.round(self.action[1], 1)
if self.counter % self.rewardInterval != 0:
return 0 # Check if it's Reward Time
distRate = self.prevDist - self.dist
if self.prevDist == 0:
self.prevDist = self.dist
return 0
moveTowardsGoalReward = self._rewardMoveTowardsGoal(distRate)
self.moveTowardGoalTotalReward += moveTowardsGoalReward
obsProximityPunish = self._rewardObstacleProximity()
self.obsProximityTotalPunish += obsProximityPunish
faceObstaclePunish = -self._rewardFacingObstacle()
self.faceObstacleTotalPunish += faceObstaclePunish
reward = moveTowardsGoalReward + obsProximityPunish + faceObstaclePunish
#self._printMax(moveTowardsGoalReward)
totalRewardDic = {
"faceObstacleTotalPunish": self.faceObstacleTotalPunish,
"obsProximityTotalPunish": self.obsProximityTotalPunish,
"moveTowardGoalTotalReward": self.moveTowardGoalTotalReward
}
rewardDic = {
"faceObstaclePunish": faceObstaclePunish,
"obsProximityPunish": obsProximityPunish,
"moveTowardsGoalReward": moveTowardsGoalReward,
"Total": reward,
"\033[1mTotalReward\033[0m": self.totalreward
}
if self.logging:
self._printRewards(rewardDic)
self._printRewards(totalRewardDic)
self.prevDist = self.dist
return reward
def _rewardFacingObstacle(self):
rewardFaceObs = 0
# Check forward lidars if moving forward, backward lidars if moving backward
lidarRange = [-2, 2] if self.action[0] >= 0 else [7, 11]
for i in range(lidarRange[0], lidarRange[1]):
scale = 0.15 if i in [-2, 1, 7, 10
] else 0.35 #Side readings get less weight
rewardFaceObs += scale * (
1 - (self.lidarRanges[i] / self.safetyLimit)
) if self.lidarRanges[i] < self.safetyLimit else 0
return self.faceObstacleParam * rewardFaceObs
def _rewardMoveTowardsGoal(self, distRate):
if distRate <= 0:
return self.moveAwayParam * (distRate / 0.0044)
else:
return self.moveTowardsParam * (distRate / 0.0044)
def _rewardObstacleProximity(self):
closestObstacle = min(self.lidarRanges)
if closestObstacle <= self.safetyLimit:
return self.obsProximityParam * -(
1 - (closestObstacle / self.safetyLimit))
else:
return 0
def SaveAndQuit(self):
self._write_file('eps.txt', self.currentEpisode)
with open(self.path + 'P7_NoObstacles.txt', 'a+') as f:
print('\nSave and Quit \n')
f.write(repr(self.epInfo))
f.write('\n')
f.close()
print('\tEpisodes Saved')
self.supervisor.worldReload()
def _write_file(self, filename, intval):
with open(self.path + filename, 'w') as fp:
print(f'Episodes saved: {intval}')
fp.write(str(intval))
fp.close()
def _read_file(self, filename):
with open(self.path + filename) as fp:
return int(fp.read())
def _startEpisode(self):
if self.start != 0: self._logEpisode()
# Read episode number
if self.currentEpisode == 0 and os.path.exists(self.path + 'eps.txt'):
self.currentEpisode = self._read_file('eps.txt')
else:
self.currentEpisode += 1
print('\n\t\t\t\t EPISODE No. {} \n'.format(self.currentEpisode))
self.start = time.time()
if self.needReset: # Reset object(s) / counters / rewards
print('Resetting Robot...')
self._resetObject(self.robot, self.robotResetLocation)
self.counter = 0
self.totalreward = 0
self.moveTowardGoalTotalReward = 0
self.obsProximityTotalPunish = 0
self.faceObstacleTotalPunish = 0
self.prevDist = 0
def _endEpisode(self, ending):
self.epConc = ending
prevMode = self.supervisor.simulationGetMode()
self.supervisor.simulationSetMode(0)
self.supervisor.exportImage(
self.logDir + "screenshots/" + ending + "/" +
str(self.currentEpisode) + ".jpg", 100)
self.supervisor.simulationSetMode(prevMode)
print(ending)
def _getState(self):
self.lidarRanges = self._trimLidarReadings(
self.lidar.getRangeImage()) # Get LidarInfo
#if self.logging: self._printLidarRanges()
self.position = self.robot.getPosition(
)[::2] # Get Position (x,y) and orientation
self.orientation = self.orientationField.getSFRotation()[3]
self.dist = self._getDist() # Get Eucledian distance from the goal
self.direction = self._getDirection()
def _printRewards(self, r, interval=1):
if self.counter % interval == 0:
print("\n\033[1mEpisode {}, timestep {}\033[0m".format(
self.currentEpisode, self.counter))
for k, v in r.items():
print("\t" + str(k) + ":\t" + str(v))
def _printMax(self, reward):
maxR = reward if self.prevMax < reward else self.prevMax
self.prevMax = maxR
print("max: {}".format(maxR))
def _printLidarRanges(self):
lidarFormatted = []
for i in range(len(self.lidarRanges)):
lidarFormatted.append(str(i) + ': ' + str(self.lidarRanges[i]))
print(lidarFormatted)
def _getLogDirectory(self):
loggingDir = "Logging/"
latestFile = ""
latestMod = 0
for file in os.listdir(loggingDir):
fileModTime = os.path.getmtime(loggingDir + file)
if fileModTime > latestMod:
latestMod = fileModTime
latestFile = file
self.logDir = loggingDir + latestFile + "/"
self.path = self.logDir + "log/"
def _logEpisode(self):
self.duration = time.time() - self.start
self.epInfo.append([
round(self.duration, 3),
round(self.totalreward, 3), self.epConc, self.counter, "Goal:",
self.goal, "Start position:", self.startPosition, "Rewards:",
self.moveTowardGoalTotalReward, self.obsProximityTotalPunish,
self.faceObstacleTotalPunish
])
