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

被引：0

作者：

Chen, Hongpeng ^{[1
]}

Huo, Shengzeng ^{[1
]}

Muddassir, Muhammad ^{[2
]}

Lee, Hoi-Yin ^{[1
]}

Liu, Yuli ^{[3
]}

Li, Junxi ^{[1
]}

Duan, Anqing ^{[4
]}

Zheng, Pai ^{[1
]}

Navarro-Alarcon, David ^{[1
]}

机构：

[1] The Hong Kong Polytechnic University (PolyU), Faculty of Engineering, Hong Kong, Hong Kong

[2] The Hong Kong Polytechnic University (PolyU), Faculty of Construction and Environment, Hong Kong, Hong Kong

[3] University of Macau, Faculty of Science and Technology, Department of Ocean Science and Technology, Taipa, China

[4] Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Department of Robotics, Abu Dhabi, United Arab Emirates

来源：

IEEE Transactions on Instrumentation and Measurement | 2025年 / 74卷

关键词：

Electronics industry - Inspection equipment - Interpolation - K-means clustering - Motion planning - Robots;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © 1963-2012 IEEE.

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