robot.getMotor('leg5_servo2'),
]
# ds = robot.getDistanceSensor('dsname')
# ds.enable(timestep)
def move_forward():
motor_lst[1 + 0*3].setPosition(math.pi * -1 / 8)
motor_lst[1 + 0*3].setVelocity(1.0)
def rotate(angle):
for i in range(6):
motor_lst[0 + i*3].setPosition(angle)
motor_lst[0 + i*3].setVelocity(1.0)
camera = Camera("camera_d435i")
camera.enable(15)
print(camera.getSamplingPeriod())
camera.saveImage("~/test.png", 100)
gyro = Gyro("gyro")
gyro.enable(60)
inertial_unit = InertialUnit("inertial_unit")
inertial_unit.enable(60)
# Main loop:
# - perform simulation steps until Webots is stopping the controller
def default_low_pos():
for i in range(6):
motor_lst[0 + i*3].setPosition(0)
motor_lst[0 + i*3].setVelocity(1.0)
# create the Robot instance.
driver = Driver()
# get the time step of the current world.
timestep = int(driver.getBasicTimeStep())
Max_hizi = 80
ileri_hizi = 20
fren = 0
sayici = 0
plot = 10
camera = driver.getCamera("camera")
Camera.enable(camera, timestep)
lms291 = driver.getLidar("Sick LMS 291")
Lidar.enable(lms291, timestep)
lms291_yatay = Lidar.getHorizontalResolution(lms291)
fig = plt.figure(figsize=(3, 3))
# Main loop:
# - perform simulation steps until Webots is stopping the controller
while driver.step() != -1:
Camera.getImage(camera)
Camera.saveImage(camera, "camera.png", 1)
frame = cv2.imread("camera.png")
positionSensors = []
# Create the Robot instance.
robot = Robot()
robot.getSupervisor()
basicTimeStep = int(robot.getBasicTimeStep())
# print(robot.getDevice("camera"))
camera1 = robot.getCamera("Camera")
print(camera1)
# camera= Camera(camera1)
camera = Camera('Camera')
# print(robot.getCamera('Camera'))
# camera.wb_camera_enable()
mTimeStep = basicTimeStep
print(camera.enable(int(mTimeStep)))
print(camera.getSamplingPeriod())
print(camera.getWidth())
print(camera.getHeight())
image = camera.getImage()
# print(image)
if image == None:
print("none")
# print(image.size())
# cameradata = cv2.VideoCapture('Camera')
camera.saveImage('/home/luyi/webots.png', 100)
# print(len(cap))
# cv2.imshow("cap",cap)
# print(image[2][3][0])
# for x in range(0,camera.getWidth()):
# for y in range(0,camera.getHeight()):
from controller import Robot, Motor, DistanceSensor
from ikpy.chain import Chain
import numpy as np
import time
from controller import Camera, Device, CameraRecognitionObject
robot = Robot()
camera = Camera("camera")
camera.enable(20)
#firstObject = Camera.getRecognitionObjects()[0]
#id = firstObject.get_id()
#position = firstObject.get_position()
robot = Robot()
global camPub
global rangePub
camPub = rospy.Publisher('/camera/image', Image, queue_size=20)
rangePub = rospy.Publisher('/range_finder/image', Image, queue_size=20)
# get the time step of the current world.
timestep = int(robot.getBasicTimeStep())
SAMPLE_TIME = 100
camera = robot.getDevice('camera')
depth = robot.getDevice('range-finder')
global rscam
global depthcam
rscam = Camera('camera')
depthcam = RangeFinder('range-finder')
depthcam.enable(SAMPLE_TIME)
rscam.enable(SAMPLE_TIME)
rospy.Subscriber('publish_images', Bool, camera_CB)
clockPublisher = rospy.Publisher('clock', Clock, queue_size=1)
if not rospy.get_param('use_sim_time', False):
rospy.logwarn('use_sim_time is not set!')
# Main loop:
# - perform simulation steps until Webots is stopping the controller
while robot.step(timestep) != -1 and not rospy.is_shutdown():
msg = Clock()
time = robot.getTime()
msg.clock.secs = int(time)
# round prevents precision issues that can cause problems with ROS timers
msg.clock.nsecs = round(1000 * (time - msg.clock.secs)) * 1.0e+6
clockPublisher.publish(msg)
from vehicle import Driver
from controller import Camera, Display, Keyboard
import cv2
import numpy as np
from numpy import array
car = Driver()
# cameraFront = Camera("cameraFront")
cameraTop = Camera("cameraTop")
display = Display("displayTop")
display.attachCamera(cameraTop)
keyboard = Keyboard()
# cameraFront.enable(32)
cameraTop.enable(32)
keyboard.enable(32)
while car.step() != -1:
display.setColor(0x000000)
display.setAlpha(0.0)
display.fillRectangle(0, 0, display.getWidth(), display.getHeight())
img = cameraTop.getImage()
image = np.frombuffer(img, np.uint8).reshape(
(cameraTop.getHeight(), cameraTop.getWidth(), 4))
# cv2.imwrite("img.png", image)
gray = cv2.cvtColor(np.float32(image), cv2.COLOR_RGB2GRAY)
#--- vira a imagem da camera em 90 graus
#gray270 = np.rot90(gray, 3)
roda_2 = robot.getMotor("wheel3")
roda_3 = robot.getMotor("wheel4")
rodas_l = [roda_0, roda_1, roda_2, roda_3]
finger_0 = robot.getMotor("finger1")
finger_1 = robot.getMotor("finger2")
joint_list = [joint_2, finger_0, finger_1]
finger_list = [finger_0, finger_1]
wj = [rodas_l, joint_list]
c = Camera("camera")
c.enable(samplingPeriod=2000)
# importing the tesseract executable again
pytesseract.pytesseract.tesseract_cmd = t_path
def robot_initial():
while robot.step(timestep) != -1:
c.enable(samplingPeriod=100)
# here the robot goes forward to the tags
for r in rodas_l:
r.setPosition(n0)
base_braço.setPosition(1.5)
delay(8500)
# here we start robot's whole process
'''.................................'''
# head + tail name pipe
read_name_list = [(i + "%s.pipe" % (channel_down + 1)) for i in read_name_down]
write_name_list = [(i + "%s.pipe" % (channel_down + 1))
for i in write_name_down]
all_path = read_name_list + write_name_list
# print(all_path)
make_pipe(all_path)
obs_pipe_down, touch_pipe_down, reward_pipe_down, over_pipe_down, terminal_pipe_down = open_write_pipe(
write_name_list)
action_pipe_down, reset_pipe_down = open_read_pipe(read_name_list)
CAM_A = Camera("CAM_A")
CAM_A.enable(32)
CAM_A.recognitionEnable(32)
CAM_B = Camera("CAM_B")
CAM_B.enable(32)
CAM_B.recognitionEnable(32)
CAM_C = Camera("CAM_C")
CAM_C.enable(32)
CAM_C.recognitionEnable(32)
CAM_D = Camera("CAM_D")
CAM_D.enable(32)
CAM_D.recognitionEnable(32)
'''
ik code------------------------------------------------------------------------------------------------------
'''
#position Sensor
positionSensorList = []
#sensörün mesafede nasıl algı m
min_uzaklık = 1.0
# create the Robot instance.
robot = Robot()
# get the time step of the current world.
timestep = int(robot.getBasicTimeStep())
#Camera ve RangeFinder getirir
kinectColor = robot.getCamera("kinect color")
kinectRange = robot.getRangeFinder("kinect range")
#camera ve rangefinder başlatıldı
Camera.enable(kinectColor, timestep)
RangeFinder.enable(kinectRange, timestep)
# motorların tagını getirir
#motorları getirir
solMotor = robot.getMotor("left wheel")
sağMotor = robot.getMotor("right wheel")
#motorları hareket etirir
solMotor.setPosition(float("inf"))
sağMotor.setPosition(float("inf"))
ps = []
psNames = ["so0", "so1", "so2", "so3", "so4", "so5", "so6", "so7"]
for i in range(8):
if print_once_flag:
print("Waiting for command")
robot.step(1)
print_once_flag = False
msg = recv_msg()
if msg != None:
cmd = pickle.loads(msg)
print(cmd)
current_task = cmd["name"]
args = cmd["args"]
print("Executing: " + current_task + " " + str(args))
print_once_flag = True
if current_task == "get_image":
if not rgb_enabled:
cameraRGB.enable(timestep)
rgb_enabled = True
else:
np_img = np.array(cameraRGB.getImageArray(), dtype=np.uint8)
respond("done", np_img)
current_task = "idle"
cameraRGB.disable()
rgb_enabled = False
if current_task == "get_depth":
if not depth_enabled:
cameraDepth.enable(timestep)
depth_enabled = True
else:
np_dep = np.array(cameraDepth.getRangeImageArray())
respond("done", np_dep)
from controller import Robot
from controller import Camera
TIME_STEP = 64
robot = Robot()
#Define motors
motors = []
motorNames = ['left motor', 'right motor']
for i in range(2):
motors.append(robot.getMotor(motorNames[i]))
motors[i].setPosition(float('inf'))
motors[i].setVelocity(0.0)
motors[i].setAcceleration(25)
camera = Camera('camera')
camera.enable(int(robot.getBasicTimeStep()))
SPEED = 2
while robot.step(TIME_STEP) != -1:
leftSpeed = 0.0
rightSpeed = 0.0
#get image and process it
image = camera.getImage()
leftSum = 0
rightSum = 0
cameraData = camera.getImage()
for x in range(0, camera.getWidth()):
for y in range(int(camera.getHeight() * 0.9), camera.getHeight()):
gray = Camera.imageGetGray(cameraData, camera.getWidth(), x, y)
if x < camera.getWidth() / 2:
leftSum += gray
else:
def clamp(x, low, hi):
return min(hi, max(low, x))
init_supervisor()
# create the Robot instance.
robot = supervisor
print("Goal Location:", goal_location)
# get the time step of the current world.
timestep = int(robot.getBasicTimeStep())
# initialize camera
camera = Camera('camera')
camera.enable(1)
# position camera
camera_pitch_motor = robot.getMotor('camera pitch')
camera_pitch_motor.setPosition(math.pi / 2)
# Start propellers
initializeMotors(robot)
prop_takeoff_speed = 68.5
motorsSpeed(robot, prop_takeoff_speed, prop_takeoff_speed, prop_takeoff_speed,
prop_takeoff_speed)
# Initialize Gyro
myGyro = robot.getGyro('gyro')
myGyro.enable(timestep)
# Initialize InertialUnit
inertial_unit = InertialUnit('inertial unit')
inertial_unit.enable(timestep)
# Initialize GPS
class SupervisorController:
def __init__(self,
timesteps=32,
gamma=0.99,
epsilon=1.0,
epsilon_min=0.01,
epsilon_log_decay=0.99,
alpha=0.01):
self.supervisor = Supervisor()
self.robot_node = self.supervisor.getFromDef("MY_BOT")
if self.robot_node is None:
sys.stderr.write(
"No DEF MY_ROBOT node found in the current world file\n")
sys.exit(1)
self.trans_field = self.robot_node.getField("translation")
self.rot_field = self.robot_node.getField("rotation")
self.timestep = timesteps
self.camera = Camera('camera')
self.camera.enable(self.timestep)
self.init_image = self.get_image()
self.timestep = timesteps
self.receiver = Receiver('receiver')
self.receiver.enable(self.timestep)
self.emitter = Emitter('emitter')
self.memory = deque(maxlen=50000)
self.batch_size = 128
self.alpha = alpha
self.gamma = gamma
self.epsion_init = epsilon
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_log_decay
self.pre_state = self.init_image
self.pre_action = -1
self.pre_go_straight = False
self.reward = 0
self.step = 0
self.max_step = 200
self.file = None
# interactive
self.feedbackProbability = 0
self.feedbackAccuracy = 1
self.PPR = False
self.feedbackTotal = 0
self.feedbackAmount = 0
self.init_model()
self.init_parametter()
def init_model(self):
self.main_network = self.build_network()
self.target_network = self.build_network()
self.agent_network = self.build_network()
self.generalise_model = self.init_gereral_model()
self.pca_model = self.init_pca_model()
def init_parametter(self):
self.epsilon = self.epsion_init
self.episode = 0
self.policy_reuse = PPR()
def init_gereral_model(self):
n_clusters = 2
return KMeans(n_clusters=n_clusters, n_init=10)
def init_pca_model(self):
n_component = 100
return PCA(n_components=100, random_state=22)
def get_image(self):
image = self.camera.getImage()
if image is None:
empty_image = np.zeros((64, 64, 3))
return Image.fromarray(empty_image.astype(np.uint8))
else:
return self.toPIL(image)
def toPIL(self, bytes_data):
imgPIL = Image.frombytes('RGBA', (64, 64), bytes_data)
imgPIL = imgPIL.convert('RGB')
return imgPIL
def image_process(self, PIL):
array = np.array(PIL)
array = array / 255
return np.reshape(array, list((1, ) + array.shape))
def save_image(self, PIL, ep, step):
PIL.save(resultsFolder + 'images/' + str(ep) + '_' + str(step) +
'.png')
def update_target(self):
self.target_network.set_weights(self.main_network.get_weights())
def observation_space(self):
return self.observation_space
def get_epsilon(self, t):
return max(
self.epsilon_min,
min(self.epsilon, 1.0 - math.log10((t + 1) * self.epsilon_decay)))
def build_network(self):
model = Sequential()
model.add(Input(shape=(64, 64, 3)))
model.add(Conv2D(4, kernel_size=8, activation='linear',
padding='same'))
model.add(MaxPooling2D((2, 2), padding='same'))
model.add(Conv2D(8, kernel_size=4, activation='linear',
padding='same'))
model.add(MaxPooling2D((2, 2), padding='same'))
model.add(
Conv2D(16, kernel_size=2, activation='linear', padding='same'))
model.add(MaxPooling2D((2, 2), padding='same'))
model.add(Flatten())
model.add(Dense(256, activation='linear'))
model.add(Dense(len(self.action_space()), activation='softmax'))
opt = Nadam(learning_rate=self.alpha)
model.compile(loss='mse', optimizer=opt)
return model
def save_reward(self, file, rewards, totals, feedbacks):
pairs = {'Reward': rewards, 'Total': totals, 'Feedback': feedbacks}
data_df = pd.DataFrame.from_dict(pairs)
data_df.to_csv(file)
def save_model(self, file):
self.main_network.save_weights(file)
self.save_generalise_model(file)
def save_generalise_model(self, filename):
obs = [s[5] for s in self.memory]
with open(filename + 'gel', "wb") as f:
pickle.dump(self.generalise_model, f)
with open(filename + 'pca', "wb") as f:
pickle.dump(self.pca_model, f)
with open(filename + 'state', "wb") as f_:
pickle.dump(obs, f_)
def load_generalise_model(self, filename):
with open(filename + 'gel', "rb") as f:
print(filename + 'gel')
self.generalise_model = pickle.load(f)
with open(filename + 'pca', "rb") as f:
self.pca_model = pickle.load(f)
def load_model(self, file):
self.agent_network.load_weights(file + '.model')
self.load_generalise_model(file + '.model')
self.update_target()
def finalise(self, rewards, totals, feedbacks, ppr):
file = self.file + '_' + str(self.feedbackProbability) + '_' + str(
self.feedbackAccuracy) + str(ppr)
self.save_reward(file + '.csv', rewards, totals, feedbacks)
self.save_model(file + '.model')
def get_group(self, state):
# nx, ny, nz = state[0].shape
# state = state.reshape(nx * ny * nz)
# state = [state]
# new_state = self.pca_model.transform(state)
nx, ny, nz = state[0].shape
image_grayscale = state[0].mean(axis=2).astype(np.float32)
image_grayscale = image_grayscale.reshape(nx * ny)
image_grayscale = [image_grayscale]
return self.generalise_model.predict(image_grayscale)[0]
def memorize(self, state, action, reward, next_state, done, obs):
self.memory.append((state, action, reward, next_state, done, obs))
def updatePolicy(self, batchSize=0):
if batchSize == 0:
batchSize = self.batch_size
if len(self.memory) < batchSize:
self.trainNetwork(len(self.memory))
return # do nothing
self.trainNetwork(batchSize)
return
def trainNetwork(self, batch_size):
# sample a mini batch of transition from the replay buffer
minibatch = random.sample(self.memory, batch_size)
states = []
targets = []
for state, action, reward, next_state, done, obs in minibatch:
state_processed = self.image_process(state)
next_state_processed = self.image_process(next_state)
if not done:
target = self.target_network.predict(next_state_processed)
target_Q = (reward + self.gamma * np.max(target[0]))
else:
target_Q = reward
# compute the Q value using the main network
Q_values = self.main_network.predict(state_processed)
Q_values[0][action] = target_Q
states.append(state_processed[0])
targets.append(Q_values[0])
# train the main network
states = np.array(states)
targets = np.array(targets)
self.main_network.fit(states, targets, epochs=1, verbose=0)
def normal_action(self, state, epsilon=0.1):
# exploration
if np.random.random() <= epsilon:
action = self.random_action()
# PPR:
if self.PPR:
group = self.get_group(state)
redoAction, rate = self.policy_reuse.get(group)
# print(group, rate)
if (np.random.rand() < rate):
action = redoAction
# end PPR:
# exploitation
else:
action = np.argmax(self.main_network.predict(state))
return action
def action_space(self):
"""
0: left
1: right
2: straight
"""
return [0, 1, 2]
def random_action(self):
# if np.random.rand() < 0.5:
# return 2
# else:
# return random.choice([0, 1])
return random.choice(self.action_space())
def propose_action(self, obs):
return
def has_obstacle(self, leftValue, rightValue):
return leftValue > 500 or rightValue > 500
def back_to_begin(self):
INITIAL = [0, 0, 0]
self.trans_field.setSFVec3f(INITIAL)
ROT_INITIAL = [0, 1, 0, 3.2]
self.rot_field.setSFRotation(ROT_INITIAL)
def reset(self):
self.pre_state = self.init_image
self.pre_action = -1
self.pre_go_straight = False
self.reward = 0
self.step = 0
self.finish = False
self.feedbackTotal = 0
self.feedbackAmount = 0
self.back_to_begin()
self.send_to_robot('reset', None)
def propose_new_action(self, obs):
left, right = obs
obstacle_flag = self.has_obstacle(left, right)
pre_state_processed = self.image_process(self.pre_state)
if not obstacle_flag:
action = 2
self.pre_action = action
self.pre_go_straight = True
else:
# propose new action ------------------
if self.PPR:
self.policy_reuse.step()
if np.random.rand() < self.feedbackProbability:
# get advice
trueAction = np.argmax(
self.agent_network.predict(pre_state_processed))
# PPR:
if self.PPR:
group = self.get_group(pre_state_processed)
self.policy_reuse.add(group, trueAction)
# end PPR:
if np.random.rand() < self.feedbackAccuracy:
action = trueAction
else:
while True:
action = self.random_action()
if action != trueAction:
break
self.feedbackAmount += 1
else:
action = self.normal_action(pre_state_processed, self.epsilon)
self.pre_go_straight = False
self.feedbackTotal += 1
self.pre_action = action
return action
def execute(self, obs, reward, done, info):
state = self.get_image()
if self.pre_action != -1:
self.reward += reward
if self.step == self.max_step or done:
if done:
self.save_image(state, self.episode, self.step)
self.save_image(self.pre_state, self.episode,
self.step - 1)
self.memorize(self.pre_state, self.pre_action, reward, state,
done, obs)
self.updatePolicy(self.step)
self.update_target()
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
self.episode += 1
self.finish = True
return
if info:
self.back_to_begin()
if not self.pre_go_straight:
self.memorize(self.pre_state, self.pre_action, reward, state,
done, obs)
self.pre_state = state
return
def receive_handle(self):
send_message, send_data = None, None
if self.receiver.getQueueLength() > 0:
data = self.receiver.getData()
message, d = pickle.loads(data)
if message == 'step_done':
obs, r, d, i, s = d
# print(s, self.step - 1, self.pre_action, r) # check synchronize
self.execute(obs, r, d, i)
if not self.finish:
action = self.propose_new_action(obs)
self.send_to_robot('step', action)
self.step += 1
if message == 'reset_done':
obs = d
self.execute(obs, 0, False, False)
action = self.propose_new_action(obs)
self.send_to_robot('step', action)
if message == 'obstacle':
self.back_to_begin()
self.receiver.nextPacket()
return
def send_to_robot(self, message, data):
data = message, data, self.step
dataSerialized = pickle.dumps(data)
self.emitter.send(dataSerialized)
def start(self,
max_step,
episodes,
file,
feedbackP=0,
feedbackA=1,
PPR=False):
self.file = file
self.max_step = max_step
self.feedbackProbability = feedbackP
self.feedbackAccuracy = feedbackA
self.PPR = PPR
rewards = []
feedbackTotal = []
feedbackAmount = []
self.init_parametter()
for i in range(episodes):
self.reset()
self.episode = i
while self.supervisor.step(
self.timestep) != -1 and not self.finish:
self.receive_handle()
print(i, self.reward, self.feedbackTotal, self.feedbackAmount)
rewards.append(self.reward)
feedbackTotal.append(self.feedbackTotal)
feedbackAmount.append(self.feedbackAmount)
self.finalise(rewards, feedbackTotal, feedbackAmount, PPR)
"""data_collector controller."""
import random
import time
import csv
import numpy as np
from controller import Robot, Camera, PositionSensor
# create the Robot instance.
robot = Robot()
# get the time step of the current world.
timestep = int(robot.getBasicTimeStep())
# get and enable Camera
cameraTop = Camera("CameraTop")
cameraTop.enable(timestep)
print("Width: ", cameraTop.getWidth())
print("Height: ", cameraTop.getHeight())
#cameraTop.saveImage("cameraImg.png", 50)
cameraBottom = Camera("CameraBottom")
cameraBottom.enable(timestep)
#Move head in right position
#HeadYaw = robot.getMotor("HeadYaw")
#HeadPitch = robot.getMotor("HeadPitch")
#HeadYaw.setPosition(0.5)
#HeadYaw.setVelocity(1.0)
#HeadPitch.setPosition(0.2)
import matplotlib.pyplot as plt
import numpy as np
import math
TIME_STEP = 64 # ms
MAX_SPEED = 100 # km/h
driver = Driver()
speedFoward = 0.1 * MAX_SPEED # km/h
speedBrake = 0 # km/h
cont = 0
plot = 10
cameraRGB = driver.getCamera('camera')
Camera.enable(cameraRGB, TIME_STEP)
lms291 = driver.getLidar('Sick LMS 291')
print(lms291)
Lidar.enable(lms291, TIME_STEP)
lms291_width = Lidar.getHorizontalResolution(lms291)
print(lms291_width)
fig = plt.figure(figsize=(3, 3))
while driver.step() != -1:
if cont < 1000:
driver.setDippedBeams(True) # farol ligado
driver.setIndicator(0) # 0 -> OFF 1 -> Right 2 -> Left
driver.setCruisingSpeed(speedFoward) # acelerador (velocidade)
# Create the Robot instance.
robot = Robot()
robot.getSupervisor()
basicTimeStep = int(robot.getBasicTimeStep())
# print(robot.getDevice("camera"))
camera1=robot.getCamera("Camera")
print(camera1)
# camera= Camera(camera1)
camera= Camera('Camera')
# print(robot.getCamera('Camera'))
# camera.wb_camera_enable()
mTimeStep=basicTimeStep
camera.enable(int(mTimeStep))
camera.getSamplingPeriod()
# width=camera.getWidth()
# height=camera.getHeight()
firstimage=camera.getImage()
ori_width = int(4 * 160) # 原始图像640x480
ori_height = int(3 * 160)
r_width = int(4 * 20) # 处理图像时缩小为80x60,加快处理速度，谨慎修改！
r_height = int(3 * 20)
color_range = {'yellow_door': [(10, 43, 46), (34, 255, 255)],
'red_floor1': [(0, 43, 46), (10, 255, 255)],
'red_floor2': [(156, 43, 46), (180, 255, 255)],
'green_bridge': [(35, 43, 20), (100, 255, 255)],
'yellow_hole': [(10, 70, 46), (34, 255, 255)],
'black_hole': [(0, 0, 0), (180, 255, 80)],
'black_gap': [(0, 0, 0), (180, 255, 100)],
"""seguidor_linha controller"""
from controller import Robot
from controller import Camera
import cv2
import numpy as np
import math
import time
robot = Robot()
timestep = int(robot.getBasicTimeStep())
#Camera
camera = Camera("camera")
camera.enable(timestep)
MAX_MOTORS = 16
L1 = 9 # UPPER_LEG_LENGTH [cm]
L2 = 8.5 # LOWER_LEG_LENGTH [cm]
M_PI = 3.14159265358979323846
gait_type = 5
i = 0
key = -1
backward = True
slf = 1
slf_min = 0
slf_max = 1
stride_length_factor = [slf, slf, slf, slf]
freq_min = 0.4
freq_max = 2
freq = 1.5
freq_offset = 0.2
class RobotController:
def __init__(self, max_steps=2, init_position=(0, 0, 0), final_position=(-0.3, 0, 0.3), max_speed=3,
):
self.robot = Robot()
self.timestep = int(self.robot.getBasicTimeStep())
self.max_steps = max_steps
self.max_speed = max_speed
self.setupDevice()
self.init_position = init_position
self.current_position = init_position
self.final_position = final_position
self.done = False
# Interactive
self.feedbackAmount = 0
# self.policy_reuse = PPR()
def setupDevice(self):
self.leftMotor = self.robot.getDevice('left wheel motor')
self.rightMotor = self.robot.getDevice('right wheel motor')
self.leftMotor.setPosition(float('inf'))
self.rightMotor.setPosition(float('inf'))
self.rightDistanceSensor = self.robot.getDevice('ds1')
self.leftDistanceSensor = self.robot.getDevice('ds0')
self.rightDistanceSensor.enable(self.timestep)
self.leftDistanceSensor.enable(self.timestep)
self.gps = self.robot.getDevice('gps')
self.touchSensor1 = self.robot.getDevice('touch_sensor1')
self.touchSensor2 = self.robot.getDevice('touch_sensor2')
self.touchSensor3 = self.robot.getDevice('touch_sensor3')
self.touchSensor4 = self.robot.getDevice('touch_sensor4')
self.touchSensor5 = self.robot.getDevice('touch_sensor5')
self.gps.enable(self.timestep)
self.touchSensor1.enable(self.timestep)
self.touchSensor2.enable(self.timestep)
self.touchSensor3.enable(self.timestep)
self.touchSensor4.enable(self.timestep)
self.touchSensor5.enable(self.timestep)
self.camera = Camera('camera')
self.camera.enable(self.timestep)
self.leftMotor.setVelocity(0)
self.rightMotor.setVelocity(0)
self.init_leftValue = self.leftDistanceSensor.getValue()
self.init_rightValue = self.rightDistanceSensor.getValue()
self.receiver = Receiver('receiver')
self.emitter = Emitter('emitter')
self.receiver.enable(self.timestep)
def is_collised(self):
if (self.touchSensor1.getValue() + self.touchSensor2.getValue() +
self.touchSensor3.getValue() + self.touchSensor4.getValue() +
self.touchSensor5.getValue() > 0):
print(1, self.touchSensor1.getValue())
print(2, self.touchSensor2.getValue())
print(3, self.touchSensor3.getValue())
print(4, self.touchSensor4.getValue())
print(5, self.touchSensor5.getValue())
return True
gpsValue = self.gps.getValues()
self.current_position = gpsValue
if (self.current_position[0] < - 0.5 or self.current_position[0] > 0.5 or
self.current_position[2] < - 0.5 or self.current_position[2] > 0.5):
return True
return False
def step(self, a):
if not self.done:
self.robot.step(self.timestep)
if not self.is_collised():
leftValue = self.leftDistanceSensor.getValue()
rightValue = self.rightDistanceSensor.getValue()
reward = -0.1
leftSpeed, rightSpeed = 0, 0
if a == 0:
leftSpeed, rightSpeed = self.turnLeft(leftValue, rightValue)
elif a == 1:
leftSpeed, rightSpeed = self.turnRight(leftValue, rightValue)
elif a == 2:
leftSpeed, rightSpeed = self.goStraight()
reward = 0
# elif a == 3:
# leftSpeed, rightSpeed = self.goSlow()
self.leftMotor.setVelocity(leftSpeed)
self.rightMotor.setVelocity(rightSpeed)
# set observation .............
observations = leftValue, rightValue
# set reward .............
# set done .........
r = self.set_done()
return observations, reward + r, self.done, False
else:
observations = self.reset()
reward = -100
return observations, reward, False, True
return None, None, self.done, False
def set_done(self):
gpsValue = self.gps.getValues()
self.current_position = gpsValue
if abs(self.current_position[0] - self.final_position[0]) <= 0.08 and \
abs(self.current_position[2] - self.final_position[2]) <= 0.08:
self.done = True
return 1000
return 0
def random_action(self):
return random.choice(self.action_space())
def goStraight(self):
return self.max_speed, self.max_speed
def goSlow(self):
return self.max_speed / 4, self.max_speed / 4
def turnLeft(self, leftDistance, rightDistance):
return -(leftDistance / 100), (rightDistance / 100) + 0.5
def turnRight(self, leftDistance, rightDistance):
return (leftDistance / 100) + 0.5, -(rightDistance / 100)
def reset(self):
self.done = False
return self.init_leftValue, self.init_rightValue
def send_to_super(self, message, data):
data = message, data
dataSerialized = pickle.dumps(data)
self.emitter.send(dataSerialized)
def receive_handle(self):
if self.receiver.getQueueLength() > 0:
data = self.receiver.getData()
message, action, step = pickle.loads(data)
self.receiver.nextPacket()
if message == 'step':
obs, r, d, i = self.step(action)
data = obs, r, d, i, step
self.send_to_super('step_done', data)
if message == 'reset':
obs = self.reset()
self.send_to_super('reset_done', obs)
return -1
def start(self):
while self.robot.step(self.timestep) != -1:
self.receive_handle()
'''.................................'''
# head + tail name pipe
read_name_list = [(i + "%s.pipe" % (channel_down + 1)) for i in read_name_down]
write_name_list = [(i + "%s.pipe" % (channel_down + 1))
for i in write_name_down]
all_path = read_name_list + write_name_list
# print(all_path)
make_pipe(all_path)
obs_pipe_down, touch_pipe_down, reward_pipe_down, over_pipe_down, terminal_pipe_down = open_write_pipe(
write_name_list)
action_pipe_down, reset_pipe_down = open_read_pipe(read_name_list)
CAM_A = Camera("CAM_A")
CAM_A.enable(32)
CAM_A.recognitionEnable(32)
'''
initial_obs, initial_state = initial_step()
write_to_pipe([obs_pipe, touch_pipe], [initial_obs, initial_state])
print(np.array(initial_obs).shape, initial_state)
'''
initial_state = initial_step()
print("init_state: {}".format(initial_state))
write_to_pipe(touch_pipe, initial_state)
class TaskType(Enum):
UP = 'up_dopamine'
DOWN = 'down_dopamine'
GRAB = 'grab_ik'
MAX_SPEED = 6.28
# maximal value returned by the sensors
MAX_SENSOR_VALUE = 1024
# minimal distance, in meters, for an obstacle to be considered
MIN_DISTANCE = 1.0
# create the robot instance
robot = Robot()
# inicializa kinect
kinectColor = robot.getCamera('kinect color')
kinectDepth = robot.getRangeFinder('kinect range')
print(kinectColor)
Camera.enable(kinectColor, TIME_STEP)
RangeFinder.enable(kinectDepth, TIME_STEP)
# get a handler to the motors and set target position to infinity (speed control)
leftMotorFront = robot.getMotor('front left wheel')
rightMotorFront = robot.getMotor('front right wheel')
leftMotorBack = robot.getMotor('back left wheel')
rightMotorBack = robot.getMotor('back right wheel')
leftMotorFront.setPosition(float('inf'))
rightMotorFront.setPosition(float('inf'))
leftMotorBack.setPosition(float('inf'))
rightMotorBack.setPosition(float('inf'))
# initialize devices
ps = []
# this function same as above function. It focuses the bin.
def foundbin(k, l):
print("bin is detected")
robot.setSpeed(k, l)
# same as "foundtrash" method.
if ((k >= 190 and l >= 190) and (left7 >= 140 or right0 >= 140)
and (red[20] >= 100 and green[20] <= 10 and blue[20] <= 10)):
led[0].set(0)
print(" I am next to bin")
# enable camera.
camera = Camera("camera")
camera.enable(timestep * 2)
print("Camera width = ", camera.getWidth(), "Camera height =",
camera.getHeight())
# enable LEDs
led = [0] * 8
count = 0
for i in range(8):
name = "led" + str(i)
led[i] = LED(name)
robot.enableEncoders(timestep)
# enable distance and ground sensors
irLeft7 = DistanceSensor("ps7")
irLeft6 = DistanceSensor("ps6")
irLeft5 = DistanceSensor("ps5")
