Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
This work addresses efficiency in dynamic multi-agent systems, but it is incremental as it builds on prior MAPF methods.
The paper tackled the problem of improving lifelong multi-agent path-finding (LMAPF) by integrating Artificial Potential Fields (APFs) into existing algorithms, resulting in up to a 7-fold increase in system throughput for LMAPF.
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.