The deployment of mobile robots for material handling in industrial environments requires scalable coordination of large fleets in dynamic settings. This paper presents a two-layer framework that combines high-level scheduling with low-level control. Tasks are assigned and scheduled using the compositional algorithm ComSat, which generates time-parameterized routes for each robot. These schedules are then used by a distributed Model Predictive Control (MPC) system in real time to compute local reference trajectories, accounting for static and dynamic obstacles. The approach ensures safe, collision-free operation, and supports rapid rescheduling in response to disruptions such as robot failures or environmental changes. We evaluate the method in simulated 2D environments with varying road capacities and traffic conditions, demonstrating high task completion rates and robust behavior even under congestion. The modular structure of the framework allows for computational tractability and flexibility, making it suitable for deployment in complex, real-world industrial scenarios.

翻译：在工业环境中部署移动机器人进行物料搬运，需要在动态场景下实现大规模集群的可扩展协调。本文提出了一种结合高层调度与底层控制的双层框架。任务分配与调度采用组合算法ComSat，为每个机器人生成时间参数化的路径。这些调度方案随后由分布式模型预测控制（MPC）系统实时用于计算局部参考轨迹，同时考虑静态与动态障碍物。该方法确保安全、无碰撞的运行，并支持针对机器人故障或环境变化等突发状况的快速重调度。我们在模拟二维环境中评估了该方法，涵盖不同道路通行能力与交通条件，结果表明其具有高任务完成率，即使在拥堵情况下仍表现出鲁棒行为。该框架的模块化结构保证了计算可处理性与灵活性，使其适用于复杂现实工业场景的部署。