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.

自主多机器人光学检测系统越来越多地用于在过程监控和质量控制中获得在线测量。已经为静态和动态环境开发了许多用于路径规划和机器人协调的方法，并应用于不同的领域。然而，由于在线优化的快速计算要求、机器人末端执行器方向的独特特性以及复杂的大规模自由形状产品表面，这些方法可能不适用于自主多机器人光学检测系统。本文提出了一种新的任务分配方法，用于多机器人检测的协调运动规划。具体而言，（1）提出了局部鲁棒检查任务分配，以实现机器人之间高效且均衡的测量分配；(2) 无碰撞路径规划和协调运动规划是通过在机器人坐标空间中的动态搜索和冲突机器人中探测位姿或局部路径的扰动来开发的。案例研究表明，所提出的方法可以降低机器人与环境之间发生碰撞的风险，解决机器人之间的冲突，并显着且一致地缩短检测周期。