The optical scanning gauges mounted on the robots are commonly used in quality inspection, such as verifying the dimensional specification of sheet structures. Coverage path planning (CPP) significantly influences the accuracy and efficiency of robotic quality inspection. Traditional CPP strategies focus on minimizing the number of viewpoints or traveling distance of robots under the condition of full coverage inspection. The measurement uncertainty when collecting the scanning data is less considered in the free-form surface inspection. To address this problem, a novel CPP method with the optimal viewpoint sampling strategy is proposed to incorporate the measurement uncertainty of key measurement points (MPs) into free-form surface inspection. At first, the feasible ranges of measurement uncertainty are calculated based on the tolerance specifications of the MPs. The initial feasible viewpoint set is generated considering the measurement uncertainty and the visibility of MPs. Then, the inspection cost function is built to evaluate the number of selected viewpoints and the average measurement uncertainty in the field of views (FOVs) of all the selected viewpoints. Afterward, an enhanced rapidly-exploring random tree (RRT*) algorithm is proposed for viewpoint sampling using the inspection cost function and CPP optimization. Case studies, including simulation tests and inspection experiments, have been conducted to evaluate the effectiveness of the proposed method. Results show that the scanning precision of key MPs is significantly improved compared with the benchmark method.
翻译:安装在机器人身上的光学扫描仪表通常用于质量检查,例如核查床单结构的尺寸规格; 覆盖路径规划(CPP)对机器人质量检查的准确性和效率有重大影响; 传统的CPP战略侧重于在全面检查的条件下尽量减少机器人的观点或移动距离; 在免费表层检查中较少考虑采集扫描数据的测量不确定性; 为解决这一问题,建议采用带有最佳观点抽样战略的新型CPP方法,将关键测量点的测量不确定性纳入免费表层检查; 首先,根据MPs的容度规格计算计量不确定性的可行范围; 考虑到MPs测量的不确定性和可见度,初步可行的观点组产生。 然后,建立检查成本功能,以评估所有选定观点的选定观点和平均测量不确定性的数量。 之后,建议采用强化的快速勘探随机树算法,利用检查成本功能和CPP的优化来查看取样。 进行案例研究,包括模拟测试和检验结果的精确性评估; 进行抽样检查的主要方法,包括模拟和检验结果的精确性测试; 进行重大的检验结果; 进行精确性试验; 进行实验室; 进行精确性试验。