Local Solution Repair for Multi-Agent Pickup and Delivery
Overview
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.
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Reported quantity · reduction in suboptimal instances: 36000 other · Basis not reported
“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 Robotics — research abstractsSignalLocal 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.
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