Local Solution Repair for Multi-Agent Pickup and Delivery

Abstract:Multi-robot systems in automated warehouses must efficiently attend to a constant stream of pickup-and-delivery tasks while avoiding collisions. Existing Multi-Agent Pickup-and-Delivery (MAPD) solutions generate plans that are globally feasible but often locally inefficient. In large-scale, long-horizon deployments, these inefficiencies, such as robots backtracking from target goal locations and idling in congested areas, accumulate over time and degrade performance. This paper contributes Local Solution Repair (LSR), a goal replanning approach that detects suboptimal behaviors at run-time and locally improves plans to reduce conflict between agents. We demonstrate the utility of our approach by repairing solutions generated by state-of-the-art MAPD baselines, M2M and LNS-PBS. Our results in long-horizon warehouse simulations over three environments show upwards of 5.9% improvement in task throughput over current state-of-the-art methods by reducing the number of suboptimal instances by ~36k or by 14.6% with minor additional computation time.

Robotics (cs.RO); Multiagent Systems (cs.MA)

arXiv:2611.02090 [cs.RO]

(or arXiv:2611.02090v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.02090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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