Load Balance Optimization Based Multi-robot Cooperative Task Planning for Large-Scale Aerospace Structures

Regarding the manufacturing and assembling requirements of large-scale structures in the aerospace industry, the existing large-scale machine tool integral manufacturing and offline split manufacturing methods have multiple limitations in cost, efficiency and quality. Taking advantages of flexibility, multi-robot system can be used for in-situ machining of large cabin, which has received extensive attention in the field of aerospace manufacturing. However, the research on multi-robot cooperative task planning is still insufficient, and there are problems such as uneven task allocation and difficult to eliminate interference between robots. This paper proposes a multi-robot cooperative task planning method to solve the problem of unbalanced load of multi-robot systems in the processing of large structures. The proposed algorithm utilizes the K-Means clustering principle to determine the base position of robots according to the density of task. The auction algorithm combined with the load distribution is studied to coordinate the assignment of tasks. The collision avoidance algorithm based on the unilateral coordination strategy is proposed to optimize the processing path of robots. The effectiveness of the algorithm in terms of time efficiency under different working conditions is demonstrated by the simulation of the large wing structure assembly. The results proved that the proposed algorithm effectively reduces the completion time and achieves the load balance of the multi-robot system. The proposed algorithm plans a conflict-free path and improves the efficiency of the multi-robot system.