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controller2d.py
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controller2d.py
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#!/usr/bin/env python3
"""
XingYu Wang
Personal project - Self-Driving Vehicle Control
Jul 24, 2019
"""
"""
2D Controller Class to be used for the CARLA waypoint follower demo.
"""
import cutils
import numpy as np
class Controller2D(object):
def __init__(self, waypoints):
self.vars = cutils.CUtils()
self._current_x = 0
self._current_y = 0
self._current_yaw = 0
self._current_speed = 0
self._desired_speed = 0
self._current_frame = 0
self._current_timestamp = 0
self._start_control_loop = False
self._set_throttle = 0
self._set_brake = 0
self._set_steer = 0
self._waypoints = waypoints
self._conv_rad_to_steer = 180.0 / 70.0 / np.pi
self._pi = np.pi
self._2pi = 2.0 * np.pi
def update_values(self, x, y, yaw, speed, timestamp, frame):
self._current_x = x
self._current_y = y
self._current_yaw = yaw
self._current_speed = speed
self._current_timestamp = timestamp
self._current_frame = frame
if self._current_frame:
self._start_control_loop = True
def update_desired_speed(self):
min_idx = 0
min_dist = float("inf")
desired_speed = 0
for i in range(len(self._waypoints)):
dist = np.linalg.norm(np.array([
self._waypoints[i][0] - self._current_x,
self._waypoints[i][1] - self._current_y]))
if dist < min_dist:
min_dist = dist
min_idx = i
if min_idx < len(self._waypoints)-1:
desired_speed = self._waypoints[min_idx][2]
else:
desired_speed = self._waypoints[-1][2]
self._desired_speed = desired_speed
def update_waypoints(self, new_waypoints):
self._waypoints = new_waypoints
def get_commands(self):
return self._set_throttle, self._set_steer, self._set_brake
def set_throttle(self, input_throttle):
# Clamp the throttle command to valid bounds
throttle = np.fmax(np.fmin(input_throttle, 1.0), 0.0)
self._set_throttle = throttle
def set_steer(self, input_steer_in_rad):
# Covnert radians to [-1, 1]
input_steer = self._conv_rad_to_steer * input_steer_in_rad
# Clamp the steering command to valid bounds
steer = np.fmax(np.fmin(input_steer, 1.0), -1.0)
self._set_steer = steer
def set_brake(self, input_brake):
# Clamp the steering command to valid bounds
brake = np.fmax(np.fmin(input_brake, 1.0), 0.0)
self._set_brake = brake
def update_controls(self):
######################################################
# RETRIEVE SIMULATOR FEEDBACK
######################################################
x = self._current_x
y = self._current_y
yaw = self._current_yaw
v = self._current_speed
self.update_desired_speed()
v_desired = self._desired_speed
t = self._current_timestamp
waypoints = self._waypoints
throttle_output = 0
steer_output = 0
brake_output = 0
######################################################
######################################################
# MODULE 7: DECLARE USAGE VARIABLES HERE
######################################################
######################################################
"""
Use 'self.vars.create_var(<variable name>, <default value>)'
to create a persistent variable (not destroyed at each iteration).
This means that the value can be stored for use in the next
iteration of the control loop.
Example: Creation of 'v_previous', default value to be 0
self.vars.create_var('v_previous', 0.0)
Example: Setting 'v_previous' to be 1.0
self.vars.v_previous = 1.0
Example: Accessing the value from 'v_previous' to be used
throttle_output = 0.5 * self.vars.v_previous
"""
self.vars.create_var('v_previous', 0.0)
self.vars.create_var('t_previous', 0.0)
self.vars.create_var('v_error_integral', 0.0)
# Skip the first frame to store previous values properly
if self._start_control_loop:
"""
Controller iteration code block.
Controller Feedback Variables:
x : Current X position (meters)
y : Current Y position (meters)
yaw : Current yaw pose (radians)
v : Current forward speed (meters per second)
t : Current time (seconds)
v_desired : Current desired speed (meters per second)
(Computed as the speed to track at the
closest waypoint to the vehicle.)
waypoints : Current waypoints to track
(Includes speed to track at each x,y
location.)
Format: [[x0, y0, v0],
[x1, y1, v1],
...
[xn, yn, vn]]
Example:
waypoints[2][1]:
Returns the 3rd waypoint's y position
waypoints[5]:
Returns [x5, y5, v5] (6th waypoint)
Controller Output Variables:
throttle_output : Throttle output (0 to 1)
steer_output : Steer output (-1.22 rad to 1.22 rad)
brake_output : Brake output (0 to 1)
"""
######################################################
######################################################
# MODULE 7: IMPLEMENTATION OF LONGITUDINAL CONTROLLER HERE
######################################################
######################################################
"""
Implement a longitudinal controller here. Remember that you can
access the persistent variables declared above here. For
example, can treat self.vars.v_previous like a "global variable".
"""
# in this controller, we assume no braking, so brake_output is always 0
# We use PID + feedforward method for longitudinal controller
# the dynamic model is not used here, just pure tuning of the gains
kp = 0.2
ki = 0.05
kd = 0.01
v_error = v_desired - v
dt = t - self.vars.t_previous
v_error_derivative = v_error/dt
self.vars.v_error_integral += v_error * dt
feedback = kp * v_error + ki * self.vars.v_error_integral + kd * v_error_derivative
# calculate the feedforward(predicted) throttle, the data is collect by
# running CARLA simulation at diffferent throttle level and measuring car speed
look_ahead = waypoints[len(waypoints)-1]
v_desired_forward = look_ahead[2]
if v_desired_forward <= 6:
feedforward = 0.15 + v_desired_forward/6*(0.6-0.15)
elif v_desired <= 11.5:
feedforward = 0.6 + (v_desired_forward-6)/(11.5-6)*(0.8-0.6)
else:
feedforward = 0.8 + (v_desired_forward-11.5)/85
throttle_output = feedforward + feedback
throttle_output = min(throttle_output,1)
throttle_output = max(throttle_output,0)
# Change these outputs with the longitudinal controller. Note that
# brake_output is optional and is not required to pass the
# assignment, as the car will naturally slow down over time.
brake_output = 0
######################################################
######################################################
# MODULE 7: IMPLEMENTATION OF LATERAL CONTROLLER HERE
######################################################
######################################################
"""
Implement a lateral controller here. Remember that you can
access the persistent variables declared above here. For
example, can treat self.vars.v_previous like a "global variable".
"""
# in this controller, we use pure pursuit method to design lateral controller
# the dynamic model is not used here, just pure tuning of the gains.
L = 1.5
# use the middle point in the given waypoints as the look ahead target
look_ahead = waypoints[len(waypoints)//2]
ld = np.sqrt((look_ahead[0] - x)**2 + (look_ahead[1] - y)**2)
vector_look_ahead = [look_ahead[0] - x,look_ahead[1] - y]
vector_car = [np.cos(yaw),np.sin(yaw)]
corss_track_error = np.cross(vector_look_ahead, vector_car)
curvature = 2/ld/ld*corss_track_error
# Change the steer output with the lateral controller.
steer_output = np.arctan(curvature*L)
steer_output = -steer_output
######################################################
# SET CONTROLS OUTPUT
######################################################
self.set_throttle(throttle_output) # in percent (0 to 1)
self.set_steer(steer_output) # in rad (-1.22 to 1.22)
self.set_brake(brake_output) # in percent (0 to 1)
#print(throttle_output,steer_output)
######################################################
######################################################
# MODULE 7: STORE OLD VALUES HERE (ADD MORE IF NECESSARY)
######################################################
######################################################
"""
Use this block to store old values (for example, we can store the
current x, y, and yaw values here using persistent variables for use
in the next iteration)
"""
self.vars.v_previous = v # Store forward speed to be used in next step
self.vars.t_previous = t # Store forward speed to be used in next step