The automatic inspection of surface defects is an important task for quality control in the computers, communications, and consumer electronics (3C) industry. Conventional devices for defect inspection (viz. line-scan sensors) have a limited field of view, thus, a robot-aided defect inspection system needs to scan the object from multiple viewpoints. Optimally selecting the robot's viewpoints and planning a path is regarded as coverage path planning (CPP), a problem that enables inspecting the object's complete surface while reducing the scanning time and avoiding misdetection of defects. However, the development of CPP strategies for robotic line scanners has not been sufficiently studied by researchers. To fill this gap in the literature, in this paper, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. Our proposed solution consists of generating a local path by a new hybrid region segmentation method and an adaptive planning algorithm to ensure the coverage of the complete object surface. An optimization method for the global path sequence is developed to maximize the scanning efficiency. To verify our proposed methodology, we conduct detailed simulation-based and experimental studies on various free-form workpieces, and compare its performance with a state-of-the-art solution. The reported results demonstrate the feasibility and effectiveness of our approach.

翻译：表面缺陷的自动检测是计算机、通信和消费电子（3C）行业质量控制的重要任务。传统缺陷检测设备（如线扫描传感器）的视场有限，因此机器人辅助缺陷检测系统需要从多个视点对物体进行扫描。最优地选择机器人视点并规划路径被视为覆盖路径规划（CPP）问题，该问题能够在减少扫描时间并避免缺陷漏检的同时，实现对物体完整表面的检测。然而，针对机器人线扫描仪的CPP策略研究尚不充分。为填补这一文献空白，本文提出了一种新的机器人线扫描方法，用于自动检测3C自由曲面物体的表面缺陷。我们的解决方案包括：通过一种新的混合区域分割方法生成局部路径，并结合自适应规划算法以确保覆盖物体完整表面；同时开发了一种全局路径序列优化方法以最大化扫描效率。为验证所提方法，我们在多种自由曲面工件上进行了详细的仿真和实验研究，并与当前最优解决方案进行了性能比较。报告的结果证明了我们方法的可行性和有效性。