Local Solution Repair for Multi-Agent Pickup and Delivery
Local Solution Repair for Multi-Agent Pickup and Delivery
This research introduces Local Solution Repair (LSR), a runtime replanning technique for multi-robot systems in automated warehouses. It addresses inefficiencies in existing Multi-Agent Pickup-and-Delivery (MAPD) solutions, such as robot backtracking and idling in congested areas. By detecting suboptimal behaviors at run-time and locally improving plans to reduce agent conflicts, LSR achieves up to a 5.9% improvement in task throughput over current state-of-the-art methods (M2M and LNS-PBS) in long-horizon warehouse simulations across three environments, reducing the number of suboptimal instances by approximately 36k with only minor additional computation time.
The paper proposes a goal replanning method called Local Solution Repair (LSR) that detects and corrects suboptimal behaviors in multi-agent pickup-and-delivery plans during execution, achieving up to 5.9% higher task throughput in long-horizon warehouse simulations by reducing suboptimal instances by approximately 36k compared to state-of-the-art baselines M2M and LNS-PBS.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
reduction in suboptimal instances
≈36,000
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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.
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arXiv:2611.02090v1 Announce Type: new 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
Open source S2
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.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org