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This paper presents the coupling-degree-based heuristic prioritized planning method (CDH-PP) to improve the efficiency and robustness of swarm path planning. The proposed method decouples the swarm path generation problem into a series of single-UAV path planning problems. And the couplingdegree-based priority heuristic generation criterion is tailored to determine the planning order of UAV swarms. The swarm coupling degree matrix is established to determine the coupling relations among UAV swarms based on the collision-detection algorithm. Anytime repairing sparse A*algorithm (AR-SAS) is adopted to plan path for single-UAV considering obstacles and inter-UAV collisions. Simulation results show the proposed CDH-PP method can generate collision-free paths for UAV swarms. And the comparative studies with the random prioritized planning and distance-based heuristic prioritized planning method demonstrate higher computational efficiency and robustness of CDH-PP.

This paper presents a receding path planning method using priority-based artificial potential field (PAPF) for UAV swarms considering moving obstacle and inter-UAV collision avoidance constraint. To enhance computational efficiency, PAPF is proposed to decouple the UAV swarm cooperative path planning problem into a series of single-UAV path planning problems. The receding planning mechanism is used to reduce the horizon of the path planning to further reduce the computational burden. In each planning horizon, when sequentially generating the paths of multiple UAVs, lower priority UAVs treat the path points of higher priority ones as obstacles for collision avoidance. Simulation studies show that PAPF can generally generate swarm paths in around Is on the scenario involving 16 UAVs, which verifies the efficiency merit of PAPF in solving dynamic path planning problems for UAV swarms.