用人工势场提升多智能体长期路径规划效率
Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields
- 将人工势场融入多种路径规划算法，避免碰撞
- 在长期任务中系统吞吐量最高提升7倍
- 特别适合动态目标持续生成的场景
我们研究了使用人工势场（APFs）解决多智能体路径规划（MAPF）和长期多智能体路径规划（LMAPF）问题。在MAPF中，一组智能体需到达目标位置而不发生碰撞；而在LMAPF中，智能体抵达目标后会生成新目标。我们提出将APFs应用于多种MAPF算法，包括优先规划、MAPF-LNS2和带回溯的优先继承（PIBT）。实验结果表明，使用APF对MAPF无明显益处，但在LMAPF中可使整体系统吞吐量最高提升7倍。
原文摘要 · Abstract (English)
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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