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Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2 #1168

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Merged
merged 4 commits into from
Feb 20, 2025
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Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2 #1168

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6edd3df
Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2
Aglargil Feb 19, 2025
eab4d46
Update move_to_pose
Aglargil Feb 20, 2025
89749b2
Add move_to_pose test
Aglargil Feb 20, 2025
47db764
Update move_to_pose
Aglargil Feb 20, 2025
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77 changes: 52 additions & 25 deletions Control/move_to_pose/move_to_pose.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,21 @@
Author: Daniel Ingram (daniel-s-ingram)
Atsushi Sakai (@Atsushi_twi)
Seied Muhammad Yazdian (@Muhammad-Yazdian)
Wang Zheng (@Aglargil)

P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7

"""

import matplotlib.pyplot as plt
import numpy as np
from random import random
import sys
import pathlib

sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
from utils.angle import angle_mod
from random import random


class PathFinderController:
"""
Expand Down Expand Up @@ -71,13 +77,18 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
# from 0 rad to 2*pi rad with slight turn

rho = np.hypot(x_diff, y_diff)
alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
beta = angle_mod(theta_goal - theta - alpha)
v = self.Kp_rho * rho
w = self.Kp_alpha * alpha - self.Kp_beta * beta

alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
beta = angle_mod(theta_goal - theta - alpha)
if alpha > np.pi / 2 or alpha < -np.pi / 2:
# recalculate alpha to make alpha in the range of [-pi/2, pi/2]
alpha = angle_mod(np.arctan2(-y_diff, -x_diff) - theta)
beta = angle_mod(theta_goal - theta - alpha)
w = self.Kp_alpha * alpha - self.Kp_beta * beta
v = -v
else:
w = self.Kp_alpha * alpha - self.Kp_beta * beta

return rho, v, w

Expand Down Expand Up @@ -111,8 +122,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
x_diff = x_goal - x
y_diff = y_goal - y

rho, v, w = controller.calc_control_command(
x_diff, y_diff, theta, theta_goal)
rho, v, w = controller.calc_control_command(x_diff, y_diff, theta, theta_goal)

if abs(v) > MAX_LINEAR_SPEED:
v = np.sign(v) * MAX_LINEAR_SPEED
Expand All @@ -126,10 +136,22 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):

if show_animation: # pragma: no cover
plt.cla()
plt.arrow(x_start, y_start, np.cos(theta_start),
np.sin(theta_start), color='r', width=0.1)
plt.arrow(x_goal, y_goal, np.cos(theta_goal),
np.sin(theta_goal), color='g', width=0.1)
plt.arrow(
x_start,
y_start,
np.cos(theta_start),
np.sin(theta_start),
color="r",
width=0.1,
)
plt.arrow(
x_goal,
y_goal,
np.cos(theta_goal),
np.sin(theta_goal),
color="g",
width=0.1,
)
plot_vehicle(x, y, theta, x_traj, y_traj)


Expand All @@ -144,16 +166,16 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover
p2 = np.matmul(T, p2_i)
p3 = np.matmul(T, p3_i)

plt.plot([p1[0], p2[0]], [p1[1], p2[1]], 'k-')
plt.plot([p2[0], p3[0]], [p2[1], p3[1]], 'k-')
plt.plot([p3[0], p1[0]], [p3[1], p1[1]], 'k-')
plt.plot([p1[0], p2[0]], [p1[1], p2[1]], "k-")
plt.plot([p2[0], p3[0]], [p2[1], p3[1]], "k-")
plt.plot([p3[0], p1[0]], [p3[1], p1[1]], "k-")

plt.plot(x_traj, y_traj, 'b--')
plt.plot(x_traj, y_traj, "b--")

# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
"key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
)

plt.xlim(0, 20)
plt.ylim(0, 20)
Expand All @@ -162,26 +184,31 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover


def transformation_matrix(x, y, theta):
return np.array([
[np.cos(theta), -np.sin(theta), x],
[np.sin(theta), np.cos(theta), y],
[0, 0, 1]
])
return np.array(
[
[np.cos(theta), -np.sin(theta), x],
[np.sin(theta), np.cos(theta), y],
[0, 0, 1],
]
)


def main():

for i in range(5):
x_start = 20.0 * random()
y_start = 20.0 * random()
theta_start: float = 2 * np.pi * random() - np.pi
x_goal = 20 * random()
y_goal = 20 * random()
theta_goal = 2 * np.pi * random() - np.pi
print(f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n")
print(f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n")
print(
f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n"
)
print(
f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n"
)
move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal)


if __name__ == '__main__':
if __name__ == "__main__":
main()