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 multifacet...

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Published inIEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 12
Main Authors Chen, Hongpeng, Huo, Shengzeng, Muddassir, Muhammad, Lee, Hoi-Yin, Liu, Yuli, Li, Junxi, Duan, Anqing, Zheng, Pai, Navarro-Alarcon, David
Format Journal Article
LanguageEnglish
Published New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive algorithms
and consumer (3C) components
Cameras
Cluster analysis
Clustering
communications
Computers
coverage path planning (CPP)
Field of view
Free form
Image segmentation
Inspection
line-scan sensor
Particle swarm optimization
Path planning
Point cloud compression
Quality control
robotic inspection
Robotics
Robots
Scanners
Sea surface
Sensors
Service robots
Simulation
Surface defects
surface inspection
Three-dimensional displays
Vector quantization
Workpieces
Online AccessGet full text
ISSN0018-9456
1557-9662
DOI10.1109/TIM.2025.3552466

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Abstract 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.
AbstractList 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.
Author Li, Junxi
Lee, Hoi-Yin
Liu, Yuli
Muddassir, Muhammad
Zheng, Pai
Navarro-Alarcon, David
Huo, Shengzeng
Duan, Anqing
Chen, Hongpeng
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Snippet The automatic inspection of surface defects is an essential task for quality control in the computers, communications, and consumer (3C) electronics industry....
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SubjectTerms Adaptive algorithms
and consumer (3C) components
Cameras
Cluster analysis
Clustering
communications
Computers
coverage path planning (CPP)
Field of view
Free form
Image segmentation
Inspection
line-scan sensor
Particle swarm optimization
Path planning
Point cloud compression
Quality control
robotic inspection
Robotics
Robots
Scanners
Sea surface
Sensors
Service robots
Simulation
Surface defects
surface inspection
Three-dimensional displays
Vector quantization
Workpieces
Title PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components With a Robotic Line Scanner
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