The following sample is an addition to the Hungarian algorithm described in the previous article to allow a drone to calculate its flight path from the current to the next position using the shortest possible route. It uses the dubins package from PyPI — a lightweight and widely used implementation for shortest-path planning under curvature constraints.
#! /usr/bin/python
# pip install dubins matplotlib numpy
import dubins
import numpy as np
import matplotlib.pyplot as plt
def compute_dubins_path(
start_pos: tuple[float, float, float],
end_pos: tuple[float, float, float],
turning_radius: float = 10.0,
step_size: float = 1.0
):
"""
Compute the shortest Dubins path between two configurations.
Parameters:
start_pos: (x, y, heading in radians)
end_pos: (x, y, heading in radians)
turning_radius: Minimum turning radius of the drone
step_size: Distance between sampled points along the path
Returns:
List of (x, y, heading) tuples along the path
"""
path = dubins.shortest_path(start_pos, end_pos, turning_radius)
configurations, _ = path.sample_many(step_size)
return configurations
# Example usage
if __name__ == "__main__":
# Initial and final positions: (x, y, heading in radians)
q0 = (0.0, 0.0, np.deg2rad(0)) # Start at origin, facing east
q1 = (50.0, 30.0, np.deg2rad(90)) # End at (50,30), facing north
path_points = compute_dubins_path(q0, q1, turning_radius=15.0, step_size=0.5)
# Plotting the path
x_vals, y_vals = zip(*[(x, y) for x, y, _ in path_points])
plt.figure(figsize=(8, 6))
plt.plot(x_vals, y_vals, label="Dubins Path", linewidth=2)
plt.scatter(*q0[:2], color='green', label="Start")
plt.scatter(*q1[:2], color='red', label="End")
plt.axis("equal")
plt.title("Dubins Path for Drone Flight")
plt.xlabel("X")
plt.ylabel("Y")
plt.legend()
plt.grid(True)
plt.show()
#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EYzGgu5Fc4dEoCUHWQYxMbUBSfvC36iKh8ESBaLtozvdqA?e=uXE4Jm
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