Coordinating Large-Scale Robot Networks with Motion and Communication Uncertainties for Logistics Applications
This work addresses coordination challenges for large-scale robot networks in logistics, such as warehouses, but is incremental as it builds on hierarchical planning and existing algorithms like A*.
The paper tackles the problem of task allocation, path planning, and motion coordination for large-scale robot networks with thousands of robots in logistics applications, addressing motion and communication uncertainties, and validates the approach through simulations with over a thousand robots and real experiments.
In this paper, we focus on the problem of task allocation, cooperative path planning and motion coordination of the large-scale system with thousands of robots, aiming for practical applications in robotic warehouses and automated logistics systems. Particularly, we solve the life-long planning problem and guarantee the coordination performance of large-scale robot network in the presence of robot motion uncertainties and communication failures. A hierarchical planning and coordination structure is presented. The environment is divided into several sectors and a dynamic traffic heat-map is generated to describe the current sector-level traffic flow. In task planning level, a greedy task allocation method is implemented to assign the current task to the nearest free robot and the sector-level path is generated by comprehensively considering the traveling distance, the traffic heat-value distribution and the current robot/communication failures. In motion coordination level, local cooperative A* algorithm is implemented in each sector to generate the collision-free road-level path of each robot in the sector and the rolling planning structure is introduced to solve problems caused by motion and communication uncertainties. The effectiveness and practical applicability of the proposed approach are validated by large-scale simulations with more than one thousand robots and real laboratory experiments.