-
Notifications
You must be signed in to change notification settings - Fork 4
/
controller2d.py
217 lines (187 loc) · 9.17 KB
/
controller2d.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
#!/usr/bin/env python3
"""
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._lookahead_distance = 2.0
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 get_lookahead_index(self, lookahead_distance):
min_idx = 0
min_dist = float("inf")
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
total_dist = min_dist
lookahead_idx = min_idx
for i in range(min_idx + 1, len(self._waypoints)):
if total_dist >= lookahead_distance:
break
total_dist += np.linalg.norm(np.array([
self._waypoints[i][0] - self._waypoints[i-1][0],
self._waypoints[i][1] - self._waypoints[i-1][1]]))
lookahead_idx = i
return lookahead_idx
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
self._desired_speed = self._waypoints[min_idx][2]
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
self.vars.create_var('kp', 0.50)
self.vars.create_var('ki', 0.30)
self.vars.create_var('integrator_min', 0.0)
self.vars.create_var('integrator_max', 10.0)
self.vars.create_var('kd', 0.13)
self.vars.create_var('kp_heading', 8.00)
self.vars.create_var('k_speed_crosstrack', 0.00)
self.vars.create_var('cross_track_deadband', 0.01)
self.vars.create_var('x_prev', 0.0)
self.vars.create_var('y_prev', 0.0)
self.vars.create_var('yaw_prev', 0.0)
self.vars.create_var('v_prev', 0.0)
self.vars.create_var('t_prev', 0.0)
self.vars.create_var('v_error', 0.0)
self.vars.create_var('v_error_prev', 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:
self.vars.v_error = v_desired - v
self.vars.v_error_integral += self.vars.v_error * \
(t - self.vars.t_prev)
v_error_rate_of_change = (self.vars.v_error - self.vars.v_error_prev) /\
(t - self.vars.t_prev)
# cap the integrator sum to a min/max
self.vars.v_error_integral = \
np.fmax(np.fmin(self.vars.v_error_integral,
self.vars.integrator_max),
self.vars.integrator_min)
throttle_output = self.vars.kp * self.vars.v_error +\
self.vars.ki * self.vars.v_error_integral +\
self.vars.kd * v_error_rate_of_change
# Find cross track error (assume point with closest distance)
crosstrack_error = float("inf")
crosstrack_vector = np.array([float("inf"), float("inf")])
ce_idx = self.get_lookahead_index(self._lookahead_distance)
crosstrack_vector = np.array([waypoints[ce_idx][0] - \
x - self._lookahead_distance*np.cos(yaw),
waypoints[ce_idx][1] - \
y - self._lookahead_distance*np.sin(yaw)])
crosstrack_error = np.linalg.norm(crosstrack_vector)
# set deadband to reduce oscillations
#print(crosstrack_error)
if crosstrack_error < self.vars.cross_track_deadband:
crosstrack_error = 0.0
# Compute the sign of the crosstrack error
crosstrack_heading = np.arctan2(crosstrack_vector[1],
crosstrack_vector[0])
crosstrack_heading_error = crosstrack_heading - yaw
crosstrack_heading_error = \
(crosstrack_heading_error + self._pi) % \
self._2pi - self._pi
crosstrack_sign = np.sign(crosstrack_heading_error)
# Compute heading relative to trajectory (heading error)
# First ensure that we are not at the last index. If we are,
# flip back to the first index (loop the waypoints)
if ce_idx < len(waypoints)-1:
vect_wp0_to_wp1 = np.array(
[waypoints[ce_idx+1][0] - waypoints[ce_idx][0],
waypoints[ce_idx+1][1] - waypoints[ce_idx][1]])
trajectory_heading = np.arctan2(vect_wp0_to_wp1[1],
vect_wp0_to_wp1[0])
else:
vect_wp0_to_wp1 = np.array(
[waypoints[0][0] - waypoints[-1][0],
waypoints[0][1] - waypoints[-1][1]])
trajectory_heading = np.arctan2(vect_wp0_to_wp1[1],
vect_wp0_to_wp1[0])
heading_error = trajectory_heading - yaw
heading_error = \
(heading_error + self._pi) % self._2pi - self._pi
# Compute steering command based on error
steer_output = heading_error + \
np.arctan(self.vars.kp_heading * \
crosstrack_sign * \
crosstrack_error / \
(v + self.vars.k_speed_crosstrack))
######################################################
# 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)
self.vars.x_prev = x
self.vars.y_prev = y
self.vars.yaw_prev = yaw
self.vars.v_prev = v
self.vars.v_error_prev = self.vars.v_error
self.vars.t_prev = t