Autonomous drilling and deburring processing planning method of multi-robot systems

Large workpieces are characterized by enormous size and complex structures. Traditional single-machine manufacturing methodologies encounter challenges such as inefficiency, inconsistent quality, and spatial constraints. Conversely, multi-robot systems exhibit high processing flexibility, providing a suitable foundation for producing large workpieces. However, as the number of tasks and robots increases, programming methods that rely on human instruction will become increasingly complex and inefficient. Consequently, this paper proposes a multi-process trajectory planning procedure for multi-robot systems. This paper realizes multi-robot and multi-task allocation, path planning, and collision avoidance based on the distributed optimization strategy of predicting subsequent constraints. The gray wolf algorithm, augmented with local search strategies, is utilized for task allocation and path planning. The processing trajectory is further optimized by rotating the starting point and task process direction. Ultimately, a delay-waiting-based time scheduling algorithm is employed to ensure the safety and reliability of the trajectory. This paper validates the planning efficacy of the entire process through simulations. The results validate the effectiveness of the proposed procedure in planning processing trajectories for large workpieces, ensuring optimal processing efficiency while maintaining the safety and reliability of the trajectory.