Multiagent Rollout with Reshuffling for Warehouse Robots Path Planning

TL;DR

This paper introduces a multiagent rollout with reshuffling algorithm for warehouse robot path planning, offering a new simulation-based approach with theoretical guarantees and adaptability to dynamic changes.

Contribution

It presents a novel multiagent rollout with reshuffling algorithm based on a new framework, improving path planning efficiency and adaptability in warehouse environments.

Findings

Solid theoretical guarantee for the proposed scheme

Consistent performance demonstrated in numerical studies

Ability to adapt to environment changes through online replanning

Abstract

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.