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Control.py
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Control.py
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#!/usr/bin/env python
import rospy
from time import time
from std_msgs.msg import String
from geometry_msgs.msg import Twist
from nav_msgs.msg import Odometry
from gazebo_msgs.msg import ModelState
from math import *
import numpy as np
from tf.transformations import euler_from_quaternion, quaternion_from_euler
# Q-learning speed parameters
CONST_LINEAR_SPEED_FORWARD = 0.08
CONST_ANGULAR_SPEED_FORWARD = 0.0
CONST_LINEAR_SPEED_TURN = 0.06
CONST_ANGULAR_SPEED_TURN = 0.4
# Feedback control parameters
K_RO = 2
K_ALPHA = 15
K_BETA = -3
V_CONST = 0.1 # [m/s]
# Goal reaching threshold
GOAL_DIST_THRESHOLD = 0.1 # [m]
GOAL_ANGLE_THRESHOLD = 15 # [degrees]
# Get theta in [radians]
def getRotation(odomMsg):
orientation_q = odomMsg.pose.pose.orientation
orientation_list = [ orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w]
(roll, pitch, yaw) = euler_from_quaternion(orientation_list)
return yaw
# Get (x,y) coordinates in [m]
def getPosition(odomMsg):
x = odomMsg.pose.pose.position.x
y = odomMsg.pose.pose.position.y
return ( x , y)
# Get linear speed in [m/s]
def getLinVel(odomMsg):
return odomMsg.twist.twist.linear.x
# Get angular speed in [rad/s] - z axis
def getAngVel(odomMsg):
return odomMsg.twist.twist.angular.z
# Create rosmsg Twist()
def createVelMsg(v,w):
velMsg = Twist()
velMsg.linear.x = v
velMsg.linear.y = 0
velMsg.linear.z = 0
velMsg.angular.x = 0
velMsg.angular.y = 0
velMsg.angular.z = w
return velMsg
# Go forward command
def robotGoForward(velPub):
velMsg = createVelMsg(CONST_LINEAR_SPEED_FORWARD,CONST_ANGULAR_SPEED_FORWARD)
velPub.publish(velMsg)
# Turn left command
def robotTurnLeft(velPub):
velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,+CONST_ANGULAR_SPEED_TURN)
velPub.publish(velMsg)
# Turn right command
def robotTurnRight(velPub):
velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,-CONST_ANGULAR_SPEED_TURN)
velPub.publish(velMsg)
# Stop command
def robotStop(velPub):
velMsg = createVelMsg(0.0,0.0)
velPub.publish(velMsg)
# Set robot position and orientation
def robotSetPos(setPosPub, x, y, theta):
checkpoint = ModelState()
checkpoint.model_name = 'turtlebot3_burger'
checkpoint.pose.position.x = x
checkpoint.pose.position.y = y
checkpoint.pose.position.z = 0.0
[x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta))
checkpoint.pose.orientation.x = x_q
checkpoint.pose.orientation.y = y_q
checkpoint.pose.orientation.z = z_q
checkpoint.pose.orientation.w = w_q
checkpoint.twist.linear.x = 0.0
checkpoint.twist.linear.y = 0.0
checkpoint.twist.linear.z = 0.0
checkpoint.twist.angular.x = 0.0
checkpoint.twist.angular.y = 0.0
checkpoint.twist.angular.z = 0.0
setPosPub.publish(checkpoint)
return ( x , y , theta )
# Set random initial robot position and orientation
def robotSetRandomPos(setPosPub):
x_range = np.array([-0.4, 0.6, 0.6, -1.4, -1.4, 2.0, 2.0, -2.5, 1.0, -1.0])
y_range = np.array([-0.4, 0.6, -1.4, 0.6, -1.4, 1.0, -1.0, 0.0, 2.0, 2.0])
theta_range = np.arange(0, 360, 15)
#theta_range = np.array([0, 30, 45, 60, 75, 90])
ind = np.random.randint(0,len(x_range))
ind_theta = np.random.randint(0,len(theta_range))
x = x_range[ind]
y = y_range[ind]
theta = theta_range[ind_theta]
checkpoint = ModelState()
checkpoint.model_name = 'turtlebot3_burger'
checkpoint.pose.position.x = x
checkpoint.pose.position.y = y
checkpoint.pose.position.z = 0.0
[x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta))
checkpoint.pose.orientation.x = x_q
checkpoint.pose.orientation.y = y_q
checkpoint.pose.orientation.z = z_q
checkpoint.pose.orientation.w = w_q
checkpoint.twist.linear.x = 0.0
checkpoint.twist.linear.y = 0.0
checkpoint.twist.linear.z = 0.0
checkpoint.twist.angular.x = 0.0
checkpoint.twist.angular.y = 0.0
checkpoint.twist.angular.z = 0.0
setPosPub.publish(checkpoint)
return ( x , y , theta )
# Perform an action
def robotDoAction(velPub, action):
status = 'robotDoAction => OK'
if action == 0:
robotGoForward(velPub)
elif action == 1:
robotTurnLeft(velPub)
elif action == 2:
robotTurnRight(velPub)
else:
status = 'robotDoAction => INVALID ACTION'
robotGoForward(velPub)
return status
# Feedback Control Algorithm
def robotFeedbackControl(velPub, x, y, theta, x_goal, y_goal, theta_goal):
# theta goal normalization
if theta_goal >= pi:
theta_goal_norm = theta_goal - 2 * pi
else:
theta_goal_norm = theta_goal
ro = sqrt( pow( ( x_goal - x ) , 2 ) + pow( ( y_goal - y ) , 2) )
lamda = atan2( y_goal - y , x_goal - x )
alpha = (lamda - theta + pi) % (2 * pi) - pi
beta = (theta_goal - lamda + pi) % (2 * pi) - pi
if ro < GOAL_DIST_THRESHOLD and degrees(abs(theta-theta_goal_norm)) < GOAL_ANGLE_THRESHOLD:
status = 'Goal position reached!'
v = 0
w = 0
v_scal = 0
w_scal = 0
else:
status = 'Goal position not reached!'
v = K_RO * ro
w = K_ALPHA * alpha + K_BETA * beta
v_scal = v / abs(v) * V_CONST
w_scal = w / abs(v) * V_CONST
velMsg = createVelMsg(v_scal, w_scal)
velPub.publish(velMsg)
return status
# Stability Condition
def check_stability(k_rho, k_alpha, k_beta):
return k_rho > 0 and k_beta < 0 and k_alpha > k_rho
# Strong Stability Condition
def check_strong_stability(k_rho, k_alpha, k_beta):
return k_rho > 0 and k_beta < 0 and k_alpha + 5 * k_beta / 3 - 2 * k_rho / np.pi > 0