Decentralized path planning for multiple agents in complex environments using rapidly-exploring random trees

Abstract

This thesis presents a novel approach to address the challenge of planning paths for real-world multi-agent systems operating in complex environments. The technique developed, the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMARRT) algorithm, is an extension of the 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 also presented as a core component of the DMA-RRT algorithm. This coordination strategy makes use of the tree of feasible trajectories grown in the CL-RRT algorithm to dynamically update the order in which agents plan. This reordering is based on a measure of each agent's incentive to replan and allows agents with a greater incentive to plan sooner, thus reducing the global cost and improving the team's overall performance. An extended version of the algorithm, Cooperative DMA-RRT, is also presented to introduce cooperation between agents during the path selection process. The paths generated are proven to satisfy inter-agent constraints, such as collision avoidance, and a set of simulation and experimental results verify the algorithm's performance. A small scale rover is also presented as part of a practical test platform for the DMA-RRT algorithm.