Optimizing Cooperative path-finding: A Scalable Multi-Agent RRT* with Dynamic Potential Fields

TL;DR

This paper presents MA-RRT*PF, a scalable multi-agent path-finding algorithm that combines dynamic potential fields with heuristics to improve efficiency and solution quality in dense environments.

Contribution

It introduces a novel multi-agent RRT* algorithm integrating dynamic potential fields, significantly enhancing scalability and performance in complex, crowded scenarios.

Findings

MA-RRT*PF outperforms conventional methods in dense environments.

The algorithm improves obstacle avoidance and path optimality.

Empirical results demonstrate increased efficiency and solution quality.

Abstract

Cooperative path-finding in multi-agent systems demands scalable solutions to navigate agents from their origins to destinations without conflict. Despite the breadth of research, scalability remains hampered by increased computational demands in complex environments. This study introduces the multi-agent RRT* potential field (MA-RRT*PF), an innovative algorithm that addresses computational efficiency and path-finding efficacy in dense scenarios. MA-RRT*PF integrates a dynamic potential field with a heuristic method, advancing obstacle avoidance and optimizing the expansion of random trees in congested spaces. The empirical evaluations highlight MA-RRT*PF's significant superiority over conventional multi-agent RRT* (MA-RRT*) in dense environments, offering enhanced performance and solution quality without compromising integrity. This work not only contributes a novel approach to the field of cooperative multi-agent path-finding but also offers a new perspective for practical applications in densely populated settings where traditional methods are less effective.