解决仓库中多对多机器人取送货问题，提升任务完成量。
Many-to-Many Multi-Agent Pickup and Delivery
- 设计新算法M2M，处理物品可存于多个位置的复杂场景。
- 相比现有方法，平均多完成2.2万次任务，效率显著提升。
- 适合研究智能仓储、多机器人协同系统的人员参考。
自动化仓库中的多机器人系统需持续处理取送货任务，兼顾效率与安全。以往多智能体取送货（MAPD）研究主要针对一对一场景，即每个任务有固定取货与交付点。然而真实仓库常出现多对多情况：物品（以库存单位SKU标识）可从多个位置取出或存入，形成一个NP难的四维分配问题。为解决该问题，我们提出多对多多智能体取送货算法M2M。实验对比两种变体：一种最小化任务预估时长（M2M），另一种在目标函数中融入SKU分布信息（M2M-wSKU）。在8小时仓库运行模拟中，我们的方法始终达到或超越现有最优水平，其中M2M在不同环境和库存密度下平均多完成22,000次任务。
原文摘要 · Abstract (English)
Multi-robot systems in automated warehouses must manage continuous streams of pickup-and-delivery tasks while ensuring efficiency and safety. Prior work on Multi-Agent Pickup-and-Delivery (MAPD) has largely focused on the one-to-one variant, where each task has a fixed pickup and delivery location. In contrast, real warehouses often present many-to-many MAPD scenarios, where items, tracked by stock keeping unit (SKU) identifiers, can be retrieved from or stored at multiple locations, resulting in an NP-hard four-dimensional assignment problem. To solve the many-to-many MAPD problem, we contribute our algorithm: Many-to-Many Multi-Agent Pickup and Delivery (M2M). We experiment with two variants of our algorithm: one that minimizes estimated task durations (M2M), and one which incorporates SKU distribution into the objective function (M2M-wSKU). Simulation results over 8-hour warehouse operations show that our method consistently matches or outperforms prior state of the art, with M2M completing up to 22,000 more tasks on average across different environments and warehouse inventory densities.
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