  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.
