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This research introduces the first formulation for dynamic multi-agent pickup and delivery in robotic cellular warehousing, addressing the practical challenge of evolving orders during execution. By leveraging token passing paradigms, the proposed event-triggered replanning algorithms enable localized adjustments and cooperative assistance from idle robots to minimize order flowtime. These methods offer a significant efficiency improvement over static baselines, providing a scalable solution for real-world warehouse automation where task dynamics are inherent.
Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems
arXiv:2606.05669v1 Announce Type: new Abstract: Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock-keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic Multi-Agent Pickup and Delivery problem considering internal order evolution for the first time. Building on the token passing paradigm, we propose two event-triggered online replanning algorithms. The first, Dynamic Token Passing, performs localized replanning upon order updates through add-order decomposition and priority-based token scheduling while preserving collision-free execution. The second, Cooperative Token Passing, further enables idle robots to opportunistically assist newly added pickups, improving system-level efficiency. Simulation results in RCWS environments demonstrate that the proposed methods significantly reduce order flowtime compared with static and non-cooperative baselines.
Abstract:Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock-keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic Multi-Agent Pickup and Delivery problem considering internal order evolution for the first time. Building on the token passing paradigm, we propose two event-triggered online replanning algorithms. The first, Dynamic Token Passing, performs localized replanning upon order updates through add-order decomposition and priority-based token scheduling while preserving collision-free execution. The second, Cooperative Token Passing, further enables idle robots to opportunistically assist newly added pickups, improving system-level efficiency. Simulation results in RCWS environments demonstrate that the proposed methods significantly reduce order flowtime compared with static and non-cooperative baselines.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2606.05669 [cs.RO]
(or arXiv:2606.05669v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.05669 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 249 words
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