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arXiv · 2505.22753

Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields

Arseniy Pertzovsky·Roni Stern·Ariel Felner·Roie Zivan
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Abstract

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.

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Arseniy Pertzovsky, Roni Stern, Ariel Felner, Roie Zivan. 2025-05-28. Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields. https://arxiv.org/abs/2505.22753

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