mlsanityData to 14 Oct 2026

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

arXiv 2505.22753 ↗First posted 28 May 2025cs.AI cs.MA cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 28 May 2025.

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14 of 55 references.

References · 14

  1. 2019Multi-Agent Pathfinding: Definitions, Variants, and Benchmarkscited by 199
  2. 2018Searching with Consistent Prioritization for Multi-Agent Path Findingcited by 81
  3. 2020Lifelong Multi-Agent Path Finding in Large-Scale Warehousescited by 80
  4. 2022VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learningcited by 68
  5. 2019Priority Inheritance with Backtracking for Iterative Multi-agent Path Findingcited by 57
  6. 2022LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfindingcited by 51
  7. 2023Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learningcited by 29
  8. 2017Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environmentscited by 22
  9. 2020Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learningcited by 21
  10. 2023Traffic Flow Optimisation for Lifelong Multi-Agent Path Findingcited by 15
  11. 2022DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environmentscited by 7
  12. 2021Optimizing Space Utilization for More Effective Multi-Robot Path Planningcited by 6
2 more
  1. 2021Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Findingcited by 4
  2. 2019DDM: Fast Near-Optimal Multi-Robot Path Planning using Diversified-Path and Optimal Sub-Problem Solution Database Heuristicscited by 3

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