-
Notifications
You must be signed in to change notification settings - Fork 0
/
pid_controller.py
170 lines (122 loc) · 4.42 KB
/
pid_controller.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
# -----------
# User Instructions
#
# Implement a P controller by running 100 iterations
# of robot motion. The steering angle should be set
# by the parameter tau so that:
#
# steering = -tau_p * CTE - tau_d * diff_CTE - tau_i * int_CTE
#
# where the integrated crosstrack error (int_CTE) is
# the sum of all the previous crosstrack errors.
# This term works to cancel out steering drift.
#
# Your code should print a list that looks just like
# the list shown in the video.
#
# Only modify code at the bottom!
# ------------
from math import *
import random
# ------------------------------------------------
#
# this is the robot class
#
class robot:
# --------
# init:
# creates robot and initializes location/orientation to 0, 0, 0
#
def __init__(self, length = 20.0):
self.x = 0.0
self.y = 0.0
self.orientation = 0.0
self.length = length
self.steering_noise = 0.0
self.distance_noise = 0.0
self.steering_drift = 0.0
# --------
# set:
# sets a robot coordinate
#
def set(self, new_x, new_y, new_orientation):
self.x = float(new_x)
self.y = float(new_y)
self.orientation = float(new_orientation) % (2.0 * pi)
# --------
# set_noise:
# sets the noise parameters
#
def set_noise(self, new_s_noise, new_d_noise):
# makes it possible to change the noise parameters
# this is often useful in particle filters
self.steering_noise = float(new_s_noise)
self.distance_noise = float(new_d_noise)
# --------
# set_steering_drift:
# sets the systematical steering drift parameter
#
def set_steering_drift(self, drift):
self.steering_drift = drift
# --------
# move:
# steering = front wheel steering angle, limited by max_steering_angle
# distance = total distance driven, most be non-negative
def move(self, steering, distance,
tolerance = 0.001, max_steering_angle = pi / 4.0):
if steering > max_steering_angle:
steering = max_steering_angle
if steering < -max_steering_angle:
steering = -max_steering_angle
if distance < 0.0:
distance = 0.0
# make a new copy
res = robot()
res.length = self.length
res.steering_noise = self.steering_noise
res.distance_noise = self.distance_noise
res.steering_drift = self.steering_drift
# apply noise
steering2 = random.gauss(steering, self.steering_noise)
distance2 = random.gauss(distance, self.distance_noise)
# apply steering drift
steering2 += self.steering_drift
# Execute motion
turn = tan(steering2) * distance2 / res.length
if abs(turn) < tolerance:
# approximate by straight line motion
res.x = self.x + (distance2 * cos(self.orientation))
res.y = self.y + (distance2 * sin(self.orientation))
res.orientation = (self.orientation + turn) % (2.0 * pi)
else:
# approximate bicycle model for motion
radius = distance2 / turn
cx = self.x - (sin(self.orientation) * radius)
cy = self.y + (cos(self.orientation) * radius)
res.orientation = (self.orientation + turn) % (2.0 * pi)
res.x = cx + (sin(res.orientation) * radius)
res.y = cy - (cos(res.orientation) * radius)
return res
def __repr__(self):
return '[x=%.5f y=%.5f orient=%.5f]' % (self.x, self.y, self.orientation)
############## ADD / MODIFY CODE BELOW ####################
# ------------------------------------------------------------------------
#
# run - does a single control run.
def run(param1, param2, param3):
myrobot = robot()
myrobot.set(0.0, 1.0, 0.0)
speed = 1.0 # motion distance is equal to speed (we assume time = 1)
N = 100
myrobot.set_steering_drift(10.0 / 180.0 * pi) # 10 degree bias, this will be added in by the move function, you do not need to add it below!
overcte =[]
cte = myrobot.y
for i in range(N):
d_cte = myrobot.y - cte
cte = myrobot.y
overcte.append(cte)
steering = - param1 * cte - param2 * d_cte - param3 * sum(overcte)
myrobot = myrobot.move(steering,speed)
print myrobot, steering
# Call your function with parameters of (0.2, 3.0, and 0.004)
run(0.2, 3.0, 0.004)