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

TL;DR

This paper tackles coverage path planning for automatic surface defect inspection of free-form 3C components using a robotic line scanner. It introduces a two-tier CPP framework consisting of a hybrid region segmentation to define flat inspection regions, an adaptive ROI-based local path planner to generate precise line-scan trajectories, and a PSO-based global optimizer to sequence local paths for minimum inspection time. The key contributions are the hybrid RANSAC-plus-enhanced-K-means segmentation, the adaptive ROI approach for local path Definition, and the PSO-based global path sequencing, validated through simulations and four experimental case studies that show improved path length, inspection time, and defect visibility compared with state-of-the-art methods. The results demonstrate feasibility and practical impact for high-resolution, non-contact defect inspection in the 3C industry and suggest potential extensions to other industrial contexts.

Abstract

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.

Figures (12)

• Figure 1: Conceptual representation of line scanning sensor
• Figure 2: Geometric features of 3C workpieces
• Figure 3: Framework of the proposed method
• Figure 4: Cuboid coverage generation of line scanning camera during linear motion
• Figure 5: Further region segmentation and linear path planning