2019/11/16 by Jinmingwu Jiang, Jiang, Jinmingwu, Kaigui Wu +6
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Robotic Path Planning Algorithms #Robotics (cs.RO) #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.1911.07840
openalex publication_date 2019/11/16 · openalex created_date 2021/03/01 · openalex updated_date 2026/07/28
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.