Decentralised Online Planning for Multi-Robot Warehouse Commissioning

Abstract

Warehouse commissioning is a complex task in which a team of robots needs to gather and deliver items as fast and efficiently as possible while adhering to the constraint capacity of the robots. Typical centralised control approaches can quickly become infeasible when dealing with many robots. Instead, we tackle this spatial task allocation problem via distributed planning on each robot in the system. State of the art distributed planning approaches suffer from a number of limiting assumptions and ad-hoc approximations. This paper demonstrates how to use Monte Carlo Tree Search (MCTS) to overcome these limitations and provide scalability in a more principled manner. Our simulation-based evaluation demonstrates that this translates to higher task performance, especially when tasks get more complex. Moreover, this higher performance does not come at the cost of scalability: in fact, the proposed approach scales better than the previous best approach, demonstrating excellent performance on an 8-robot team servicing a warehouse comprised of over 200 locations.