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

The automatic inspection of surface defects is an essential task for quality control in the computers, communications, and consumer (3C) electronics industry. Traditional inspection mechanisms (i.e., line-scan sensors) have a limited field of view (FOV), thus prompting the necessity for a multifaceted robotic inspection system capable of comprehensive scanning. 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. In this article, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. A two-stage region segmentation method defines the local scanning based on the random sample consensus (RANSAC) and K-means clustering to improve the inspection coverage. The proposed method also consists of an adaptive region-of-interest (ROI) algorithm to define the local scanning paths. Besides, a particle swarm optimization (PSO)-based method is used for global inspection path generation to minimize the inspection time. The developed method is validated by simulation-based and experimental studies on various free-form workpieces, and its performance is compared with that of two state-of-the-art solutions. The reported results demonstrate the feasibility and effectiveness of our proposed method.