Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field
Chongde Ren, Jinchao Chen, Chenglie Du, Wenquan Yu · 2023
Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.