Decentralized path planning for multi-agent teams in complex environments using rapidly-exploring random trees

This paper presents a novel approach to address the challenge of planning paths for multi-agent systems operating in complex environments. The algorithm developed, Decentralized Multi-Agent Rapidly-exploring Random Tree (DMA-RRT), is an extension of the Closed-loop RRT (CL-RRT) algorithm to the multi-agent case, retaining its ability to plan quickly even with complex constraints. Moreover, a merit-based token passing coordination strategy is developed to dynamically update the planning order based on a measure of each agent´s incentive to replan, derived from the CL-RRT. Agents with a greater incentive plan sooner, yielding a greater reduction of the global cost and greater improvement in the team´s overall performance. An extended version of the algorithm, Cooperative DMA-RRT, allows agents to modify others´ plans in order to select paths that reduce their combined cost and thus further improve global performance. The paths generated by both algorithms are proven to satisfy inter-agent constraints, such as collision avoidance, and a set of simulation and experimental results verify performance.