Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.
翻译:多机器人光学检查系统越来越多地用于在流程监测和质量控制中获得内线测量;为静态和动态环境制定了许多路径规划和机器人协调方法,并应用于不同领域;然而,由于对内线优化、机器人终端效应定向的独特特征以及复杂的大型自由成型产品表面的快速计算要求,这些方法可能无法适用于自主多机器人光学检查系统;本文件提出了一种新颖的任务分配方法,用于协调多机器人检查的动作规划。具体地说,(1) 提议了一种本地稳健的检查任务分配,以实现机器人之间高效和平衡的测量任务分配;(2) 通过动态搜索机器人协调空间和探测器的扰动或相冲突的机器人的本地路径,制定无碰撞路径规划和协调的动作规划。