Prediction, Planning, and Coordination of Thousand-Warehousing-Robot Networks With Motion and Communication Uncertainties

In this article, we focus on resolving the traffic flow prediction, robot path planning, and motion coordination problems in large-scale warehousing robotics systems with thousand-robot networks. The warehousing environment is partitioned into several sectors, and a hierarchical framework is developed, which includes a centralized prediction and planning level and a decentralized local coordination level. In the centralized level, a traffic flow prediction algorithm is first proposed to predict the evolution of the robot density distribution in a future horizon and estimate the future traffic heat value of each sector. Based on this, the sector-level robot path can be generated in the time-expended sector graph by comprehensively considering the traveling distance and the predicted traffic heat value and will be dynamically updated by considering the most recent traffic information. In the coordination level, local cooperative A* algorithm, incorporated with the conflict-based searching strategy, is implemented within each sector to generate conflict-free road-level paths for all the robots in the sector simultaneously, and the rolling planning scheme is utilized in order to immediately react to robot motion uncertainties and communication disconnections. The effectiveness and practical applicability of the proposed approach are validated by large-scale simulations with more than one 1000 robots and real laboratory experiments.
Note to Practitioners
—Considering practical situations and requirements in industrial warehouses and automated logistics systems, this article resolves the life-long planning and coordination problems of large-scale robot networks and ensures the practical execution performance in the presence of robot motion uncertainties and temporary communication disconnections. Our main idea is to reduce robot congestions and improve warehouse working efficiency by balancing the traffic flow in the whole environment. To achieve this, we present a traffic flow prediction algorithm to estimate the robot density distribution in a future horizon and take this information into consideration in sector-level path planning. The reliability, scalability, and the real-time performance of the proposed solution are achieved by the presented hierarchical system framework and the dynamic planning scheme. The proposed concept and approach can also be used to coordinate other large-scale systems with multirobot or multi-AGV networks. Simulation and experimental results suggest that the proposed solution is effective and practically applicable, but a saturation phenomenon of the system capacity can be observed under a very heavy workload. In the future, we will investigate the relation between the maximum system capacity and the environment structure and make further efforts to optimize the environment structure and road layout in order to improve the warehouse working efficiency.