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

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.