Abstract
Mobile robots are being widely used in smart manufacturing, and efficient task assignment and path planning for these robots is an area of high interest. In previous studies, task assignment and path planning are usually solved as separate problems, which can result in optimal solutions in their respective fields, but not necessarily optimal as an integrated problem. Meanwhile, precedence constraints exist between sequential processing operations and material delivery tasks in the manufacturing environment. Thus, those planning methods developed for warehousing and logistics may not simply apply to the environment of smart factories. In this paper, we propose an integrated task and path planning approach based on Looking-backward Search Strategy (LSS) and Regret-based Search Strategy (RSS). In the stage of task assignment, the real paths for mobile robots are identified based on the Cooperative A* (CA*) algorithm and the time and energy consumed by mobile robots and machining centers are calculated. Then a greedy strategy working with LSS or RSS is used to search reasonable task assignments in time-series, which can generate a joint optimal solution for both task assignment and path planning. We verify the validity of the proposed approach in a simulated smart factory and the results show that our approach can improve the operation efficiency of the smart factory and save the time and energy consumption effectively.
