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.

翻译：自动化仓库中的多机器人系统必须管理连续的取送货任务流，同时确保效率和安全性。先前关于多智能体取送货（MAPD）的研究主要集中于一对一变体，即每个任务具有固定的取货点和送货点。相比之下，实际仓库往往呈现多对多MAPD场景，其中通过库存单位（SKU）标识符追踪的物品可以从多个地点取货或存储到多个地点，导致产生一个NP难的四维分配问题。为解决多对多MAPD问题，我们提出了算法：多对多多智能体取送货（M2M）。我们实验了该算法的两种变体：一种最小化估计任务持续时间（M2M），另一种将SKU分布纳入目标函数（M2M-wSKU）。在8小时仓库作业的模拟结果表明，我们的方法始终匹配或超越先前的最优技术，其中M2M在不同环境和仓库库存密度下平均多完成多达22,000个任务。