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

arXiv: https://arxiv.org/abs/2505.22753 · HTML: https://mlsanity.com/paper/2505.22753/

## 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.

## Builds on (most cited first, 14 of 14)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023)
- [Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments](https://mlsanity.com/paper/1710.01447/) (2017)
- [Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2001.06627/) (2020)
- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments](https://mlsanity.com/paper/2209.02865/) (2022)
- [Optimizing Space Utilization for More Effective Multi-Robot Path Planning](https://mlsanity.com/paper/2109.04677/) (2021)
- [Distributed Multi-agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-agent Path Finding](https://mlsanity.com/paper/2107.00246/) (2021)
- [DDM: Fast Near-Optimal Multi-Robot Path Planning using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics](https://mlsanity.com/paper/1904.02598/) (2019)

## 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.