TY - GEN
T1 - A Robotic Defect Inspection System for Free-form Specular Surfaces
AU - Huo, Shengzeng
AU - Navarro-Alarcon, David
AU - Chik, David T.W.
N1 - Funding Information:
This work is supported by ASTRI (grant PO2019/399), by the Research Grants Council (grant 14203917), by the Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme (grant ZG9V), by the Key-Area Research and Development Program of Guangdong Province 2020 (project 76), and by PolyU (grants G-YBYT and 4-ZZHJ).
Publisher Copyright:
© 2021 IEEE
PY - 2021/10
Y1 - 2021/10
N2 - In this paper, we present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning. Given the mesh model of the workpiece, we implement K-means based region segmentation algorithm on the point cloud after preprocessing. Then, we take the smooth regions as input to plan the scanning path. A projection registration method that robustly localizes the object in the robot's frame is proposed for real-time workpiece localization. According to the optical features of the high-resolution line scan, we design an image processing pipeline to detect defects from the captured images. We report a detailed experimental study to validate the proposed methodology.
AB - In this paper, we present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning. Given the mesh model of the workpiece, we implement K-means based region segmentation algorithm on the point cloud after preprocessing. Then, we take the smooth regions as input to plan the scanning path. A projection registration method that robustly localizes the object in the robot's frame is proposed for real-time workpiece localization. According to the optical features of the high-resolution line scan, we design an image processing pipeline to detect defects from the captured images. We report a detailed experimental study to validate the proposed methodology.
UR - http://www.scopus.com/inward/record.url?scp=85124794093&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9561265
DO - 10.1109/ICRA48506.2021.9561265
M3 - Conference article published in proceeding or book
AN - SCOPUS:85124794093
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 11364
EP - 11370
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -