发表机构
Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对最大容量下多机器人有序存取问题，提出利用无重排布局特性的在线优先多智能体路径规划算法，实现可扩展且鲁棒的规划，完工时间随机器人数量近线性提升，处理不确定性时执行速度无显著损失。
AI 中文摘要
自动化仓库面临最大化存储密度与实现高检索吞吐量之间的根本权衡。基于谜题的存储（PBS）架构通过消除通道提升容量，但在这些高密度空间中协调多机器人因存在死锁风险而具有计算挑战性。本文针对“最大容量下的有序存取问题”提出一种新颖的多机器人公式化方法，研究聚焦于从单一边界可进入的矩形网格，在给定规划的出发序列时，需先将货物存储至满容量，再高效检索。本研究利用无重排布局的特性，弥合几何可行性与执行效率之间的差距，这些特性指导了在线优先多智能体路径规划算法，这是本文的主要贡献。与通用集中式规划器不同，该方法利用存储布局的特定不变量保证完备性并防止死锁，实现可扩展性。实验表明，该方法的完工时间随机器人数量呈近线性提升，上限为m=C，其中C为网格宽度。关键的是，支持鲁棒性的算法开销可忽略不计；系统使用鲁棒存储布局处理出发序列的不确定性，与非鲁棒基线相比，执行速度无显著损失。
英文摘要
Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput. While puzzle-based storage (PBS) architectures increase capacity by eliminating aisles, coordinating multiple robots in these high-density spaces is computationally challenging. This paper formalizes the challenge through a novel multi-robot problem formulation for ordered storage and retrieval: We consider rectangular 2D grids, where uniform-sized loads are first stored, up to full capacity, and subsequently retrieved according to prescribed arrival and departure sequences. The main contribution of this work is an online prioritized multi-agent path planning algorithm for this problem. The algorithm builds on prior work that constructs arrangements supporting sequential storage and retrieval, i.e., of one load at a time, without relocating loads. By exploiting the structural invariants of such arrangements, we achieve the scalability of decoupled planning while guaranteeing complete, deadlock-free parallel execution even at full storage density. Experiments demonstrate that the algorithm achieves near-linear improvement in makespan with respect to the number of robots, up to $C$ robots, where $C$ is the width of the grid's open side. Furthermore, the algorithm supports robust storage arrangements that accommodate bounded uncertainty in the departure sequence, with negligible impact on execution makespan.
CommentsWAFR 2026 (World Symposium on the Algorithmic Foundations of Robotics)
