Efficiently solving path planning problems for a large number of robots is critical to the successful operation of modern warehouses. The existing approaches adopt classical shortest path algorithms to plan in environments whose cells are associated with both space and time in order to avoid collision between robots. In this work, we achieve the same goal by means of simulation in a smaller static environment. Built upon the new framework introduced in (Bertsekas, 2021a), we propose multiagent rollout with reshuffling algorithm, and apply it to address the warehouse robots path planning problem. The proposed scheme has a solid theoretical guarantee and exhibits consistent performance in our numerical studies. Moreover, it inherits from the generic rollout methods the ability to adapt to a changing environment by online replanning, which we demonstrate through examples where some robots malfunction.

翻译：高效解决大量机器人的路径规划问题对于现代仓库的成功运营至关重要。现有方法采用经典最短路径算法，在同时关联空间与时间的单元环境中进行规划，以避免机器人之间的碰撞。本文通过在一个较小的静态环境下进行仿真，实现了相同目标。基于(Bertsekas, 2021a)提出的新框架，我们提出了一种带重排的多智能体Rollout算法，并将其应用于解决仓库机器人路径规划问题。该方案具有坚实的理论保证，并在数值研究中展现出稳定的性能。此外，它继承了通用Rollout方法通过在线重规划适应动态环境的能力，我们通过机器人发生故障的示例对此进行了验证。