In automated warehouses, teams of mobile robots fulfill the packaging process by transferring inventory pods to designated workstations while navigating narrow aisles formed by tightly packed pods. This problem is typically modeled as a Multi-Agent Pickup and Delivery (MAPD) problem, which is then solved by repeatedly planning collision-free paths for agents on a fixed graph, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. However, existing approaches make the limiting assumption that agents are only allowed to move pods that correspond to their current task, while considering the other pods as stationary obstacles (even though all pods are movable). This behavior can result in unnecessarily long paths which could otherwise be avoided by opening additional corridors via pod manipulation. To this end, we explore the implications of allowing agents the flexibility of dynamically relocating pods. We call this new problem Terraforming MAPD (tMAPD) and develop an RHCR-based approach to tackle it. As the extra flexibility of terraforming comes at a significant computational cost, we utilize this capability judiciously by identifying situations where it could make a significant impact on the solution quality. In particular, we invoke terraforming in response to disruptions that often occur in automated warehouses, e.g., when an item is dropped from a pod or when agents malfunction. Empirically, using our approach for tMAPD, where disruptions are modeled via a stochastic process, we improve throughput by over 10%, reduce the maximum service time (the difference between the drop-off time and the pickup time of a pod) by more than 50%, without drastically increasing the runtime, compared to the MAPD setting.

翻译：在自动化仓库中，机器人团队通过将库存货架搬运至指定工作站来完成包装流程，同时需在密集排列的货架形成的狭窄通道中导航。该问题通常被建模为多智能体取送货（MAPD）问题，并通过重复在固定图上规划无碰撞路径（如滚动时域碰撞消解（RHCR）算法）进行求解。然而，现有方法存在一个限制性假设：智能体仅允许移动与当前任务相关的货架，而将其他货架视为静态障碍物（尽管所有货架均可移动）。这种行为可能导致不必要的冗长路径——若通过货架操控开辟额外通道则可避免。为此，我们探索允许智能体灵活动态重排货架的可行性。我们将这一新问题命名为地形改造MAPD（tMAPD），并开发基于RHCR的求解方法。由于地形改造的额外灵活性会带来显著的计算开销，我们通过识别该能力对解质量产生重大影响的关键情境来审慎调用此功能。具体而言，我们在自动化仓库常见的中断事件（如货架掉落物品或智能体故障）发生时触发地形改造。实验表明，在通过随机过程模拟中断场景的tMAPD模型中，我们的方法相比标准MAPD设置，在未显著增加运行时间的情况下，将吞吐量提升超过10%，并将最大服务时间（货架交付时间与取货时间之差）降低逾50%。