基于PSO的机器人线扫描仪3C组件表面缺陷检测最优覆盖路径规划
PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components with a Robotic Line Scanner
AI总结：
本文提出了一种基于PSO的机器人线扫描仪覆盖路径规划方法，通过混合区域分割和自适应算法生成局部路径，并优化全局路径序列，实现3C自由曲面物体表面缺陷的高效自动检测。
摘要：
计算机、通信和消费电子（3C）行业中的表面缺陷自动检测是质量控制的重要任务。传统的缺陷检测设备（即线扫描传感器）视场有限，因此机器人辅助缺陷检测系统需要从多个视角扫描物体。最优选择机器人视角并规划路径被视为覆盖路径规划（CPP），该问题能够检测物体的完整表面，同时减少扫描时间并避免缺陷漏检。然而，针对机器人线扫描仪的CPP策略开发尚未得到研究人员充分研究。为填补文献中的这一空白，本文提出了一种新方法，使机器人线扫描仪能够自动检测3C自由曲面物体的表面缺陷。我们提出的解决方案包括通过一种新的混合区域分割方法和自适应规划算法生成局部路径，以确保完整物体表面的覆盖。开发了全局路径序列的优化方法以最大化扫描效率。为验证所提方法，我们在各种自由曲面工件上进行了详细的基于仿真和实验的研究，并将其性能与最先进的解决方案进行了比较。报告的结果证明了我们方法的可行性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
