Enhanced Hungarian-APF-Based Joint Mission Allocation and Trajectory Planning for UAV Swarm
Hongwei Liu, Yifei Liu, Weichen Kong, Yu Guo, Mingyang Yan, Xiaoshuai Li · 2025
For the automatic control optimization problem of Unmanned Aerial Vehicle (UAV) swarm delivery, an enhanced Hungarian-APF-based joint mission allocation and trajectory planning approach is proposed in this paper. By considering the limited UAV range, order value, and long-time slots mission allocation, we aim at optimizing the sum of value function of all UAVs while guaranteeing flight duration constraints of each UAV. In particular, an enhanced Artificial Potential Field (APF) is proposed for the obstacle avoidance trajectory planning of UAV by introducing the dynamic repulsive force, setting virtual target points, and improving the coordinate update algorithm. Simulation results show that, compared to existing algorithms, the proposed algorithm not only can significantly improve the efficiency of UAV swarm delivery, but also can reduce the length of the obstacle avoidance trajectory obviously and avoid obstacles effectively by improving enhancing the smoothness of the obstacle avoidance trajectory.