Periodic Multi-Agent Path Planning
Keywords:ROB: Motion and Path Planning, ROB: Multi-Robot Systems
AbstractMulti-agent path planning (MAPP) is the problem of planning collision-free trajectories from start to goal locations for a team of agents. This work explores a relatively unexplored setting of MAPP where streams of agents have to go through the starts and goals with high throughput. We tackle this problem by formulating a new variant of MAPP called periodic MAPP in which the timing of agent appearances is periodic. The objective with periodic MAPP is to find a periodic plan, a set of collision-free trajectories that the agent streams can use repeatedly over periods, with periods that are as small as possible. To meet this objective, we propose a solution method that is based on constraint relaxation and optimization. We show that the periodic plans once found can be used for a more practical case in which agents in a stream can appear at random times. We confirm the effectiveness of our method compared with baseline methods in terms of throughput in several scenarios that abstract autonomous intersection management tasks.
How to Cite
Kasaura, K., Yonetani, R., & Nishimura, M. (2023). Periodic Multi-Agent Path Planning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6183-6191. https://doi.org/10.1609/aaai.v37i5.25762
AAAI Technical Track on Intelligent Robotics