Robust UAV Path Planning with Obstacle Avoidance for Emergency Rescue

TL;DR

This paper introduces a novel artificial potential field algorithm combined with simulated annealing for robust UAV path planning in complex 3D environments with obstacles and no-fly zones, improving safety and efficiency.

Contribution

The paper proposes a new APF-SA algorithm that enhances UAV path planning by escaping local minima and achieving globally optimal trajectories in complex environments.

Findings

APF-SA effectively avoids obstacles in 3D scenarios.

Simulation results show improved path optimality and safety.

The method outperforms traditional algorithms in complex environments.

Abstract

The unmanned aerial vehicles (UAVs) are efficient tools for diverse tasks such as electronic reconnaissance, agricultural operations and disaster relief. In the complex three-dimensional (3D) environments, the path planning with obstacle avoidance for UAVs is a significant issue for security assurance. In this paper, we construct a comprehensive 3D scenario with obstacles and no-fly zones for dynamic UAV trajectory. Moreover, a novel artificial potential field algorithm coupled with simulated annealing (APF-SA) is proposed to tackle the robust path planning problem. APF-SA modifies the attractive and repulsive potential functions and leverages simulated annealing to escape local minimum and converge to globally optimal solutions. Simulation results demonstrate that the effectiveness of APF-SA, enabling efficient autonomous path planning for UAVs with obstacle avoidance.