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The automatic inspection of surface defects is an important task for qualitycontrol 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 systemneeds to scan the object from multiple viewpoints. Optimally selecting therobot's viewpoints and planning a path is regarded as coverage path planning(CPP), a problem that enables inspecting the object's complete surface whilereducing the scanning time and avoiding misdetection of defects. However, thedevelopment of CPP strategies for robotic line scanners has not beensufficiently studied by researchers. To fill this gap in the literature, inthis paper, we present a new approach for robotic line scanners to detectsurface defects of 3C free-form objects automatically. Our proposed solutionconsists of generating a local path by a new hybrid region segmentation methodand an adaptive planning algorithm to ensure the coverage of the completeobject surface. An optimization method for the global path sequence isdeveloped to maximize the scanning efficiency. To verify our proposedmethodology, we conduct detailed simulation-based and experimental studies onvarious free-form workpieces, and compare its performance with astate-of-the-art solution. The reported results demonstrate the feasibility andeffectiveness of our approach.

PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components with a Robotic Line Scanner