arXiv:2505.22753
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
著者: Arseniy Pertzovsky, Roni Stern, Ariel Felner, Roie Zivan
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
We explore the use of Artificial Potential Fields (APFs) to solve Multi-Agent Path Finding (MAPF) and Lifelong MAPF (LMAPF) problems. In MAPF, a team of agents must move to their goal locations without collisions, whereas in LMAPF, new goals are generated upon arrival. We propose methods for incorporating APFs in a range of MAPF algorithms, including Prioritized Planning, MAPF-LNS2, and Priority Inheritance with Backtracking (PIBT). Experimental results show that using APF is not beneficial for MAPF but yields up to a 7-fold increase in overall system throughput for LMAPF.