Lifelong Multi-Agent Path Finding in Large-Scale Warehouses


  • Jiaoyang Li University of Southern California
  • Andrew Tinka Amazon Robotics
  • Scott Kiesel Amazon Robotics
  • Joseph W. Durham Amazon Robotics
  • T. K. Satish Kumar University of Southern California
  • Sven Koenig University of Southern California



Multiagent Planning, Heuristic Search, Deterministic Planning, Coordination and Collaboration


Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.




How to Cite

Li, J., Tinka, A., Kiesel, S., Durham, J. W., Kumar, T. K. S., & Koenig, S. (2021). Lifelong Multi-Agent Path Finding in Large-Scale Warehouses. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11272-11281.



AAAI Technical Track on Multiagent Systems