受限仓库中多智能体取送货任务的动态避风港选择
Dynamic Haven Selection for Multi-Agent Pickup and Delivery in Constrained Warehouses
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中文总结 AI 辅助
针对受限仓库多智能体取送货任务的固定避风港缺陷，提出自适应SHARP方法，经大量实验验证其在总完成时间上显著优于SHARP，树形地图中位数减少16.7%
中文摘要 AI 辅助
空间高效的仓库布局通常包含单智能体宽度的过道和死端工作站，机器人在此处等待时几乎没有不阻碍其他机器人的位置。在这类受限布局下的多智能体取送货（MAPD）任务中，机器人必须接受在线取送货任务，同时维护名为“避风港（Havens）”的受保护等待位置。安全避风港撤退规划器（SHARP）引入了一种机制，将每个已提交任务的路径扩展为经验证的、返回智能体专属初始避风港的撤退路径，但固定避风港承诺会导致机器人在完成送货后前往遥远的避风港。我们提出A-sharp（自适应SHARP），该方法在任务分配时更改智能体的撤退目标。简单切换可能导致两个智能体依赖同一等待位置，或让另一条已提交路径穿过仍被占用或预留的位置。A-sharp通过对候选避风港进行可用性测试，以及保留前一个避风港直至智能体离开的待释放规则，来防止这些故障。在明确的避风港结构和安全区间路径规划（SIPP）假设下，我们证明了不变量保持和有限释放完备性：任意有限释放序列中的每个任务都能在有限时间内完成。在四张地图上针对14400组地图-智能体-数量-种子配对案例开展的72000次运行中，SHARP和A-sharp均完成了各自的14400次运行。对于总完成时间（makespan），在所有138个避风港数量多于智能体数量的配置中，采用Holm校正的预设配对比较显示，A-sharp在107个配置中显著优于SHARP，且从未显著劣于SHARP；在测试的树形地图上，中位数减少量为16.7%。
英文摘要
Space-efficient warehouse layouts often contain single-agent-width aisles and dead-end workstations where robots have few places to wait without blocking others. In Multi-Agent Pickup and Delivery (MAPD) on such constrained layouts, robots must accept online pickup-delivery tasks while preserving protected waiting locations called Havens. The Safe HAven Retreat Planner (SHARP) introduced a mechanism that extends each committed task path with a validated retreat to the agent's dedicated initial Haven, but fixed-Haven commitments can send agents toward distant Havens after deliveries. We present A-sharp (Adaptive SHARP), which changes an agent's retreat target at task assignment time. A naive switch can cause two agents to rely on the same waiting location or let another committed path pass through a location that is still occupied or reserved. A-sharp prevents these failures with an availability test for candidate Havens and a pending-release rule that keeps the previous Haven protected until the agent departs. Under explicit Haven-structure and Safe Interval Path Planning (SIPP) assumptions, we prove invariant preservation and finite-release completeness: every task in any finite release sequence is delivered in finite time. Across 72,000 runs on 14,400 paired map-agent-count-rate-seed cases over four maps, both SHARP and A-sharp complete their respective 14,400 runs. For makespan (final delivery time), a prespecified paired comparison with Holm correction over all 138 configurations with more Havens than agents finds A-sharp significantly better in 107 configurations and never significantly worse than SHARP; on the tested tree map, the median reduction is 16.7%.
发表机构
- Hokkaido University（北海道大学）
- Toyota Industries Corporation（丰田工业株式会社）
机构由 AI 辅助整理，请以论文原文为准。
