This paper presents a novel fleet management strategy for battery-powered robot fleets tasked with intra-factory logistics in an autonomous manufacturing facility. In this environment, repetitive material handling operations are subject to real-world uncertainties such as blocked passages, and equipment or robot malfunctions. In such cases, centralized approaches enhance resilience by immediately adjusting the task allocation between the robots. To overcome the computational expense, a two-step methodology is proposed where the nominal problem is solved a priori using a Monte Carlo Tree Search algorithm for task allocation, resulting in a nominal search tree. When a disruption occurs, the nominal search tree is rapidly updated a posteriori with costs to the new problem while simultaneously generating feasible solutions. Computational experiments prove the real-time capability of the proposed algorithm for various scenarios and compare it with the case where the search tree is not used and the decentralized approach that does not attempt task reassignment.

翻译：本文提出了一种针对自主制造工厂中执行厂内物流任务的电池驱动机器人车队的新型管理策略。在该环境下，重复性的物料搬运操作会受到诸如通道堵塞、设备或机器人故障等现实不确定性的影响。在此类情况下，集中式方法通过立即调整机器人间的任务分配来增强系统的弹性。为克服计算开销，提出了一种两步法：首先使用蒙特卡洛树搜索算法对名义问题进行任务分配的预先求解，生成名义搜索树。当发生中断时，通过后验方式以新问题的代价快速更新名义搜索树，同时生成可行解。计算实验证明了所提算法在各种场景下的实时能力，并与未使用搜索树的情况以及不尝试任务重分配的分散式方法进行了对比。