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Automated robotic monitoring and inspection of steel structures and bridges

Published online by Cambridge University Press:  11 January 2018

Hung Manh La ,
Tran Hiep Dinh ,
Nhan Huu Pham ,
Quang Phuc Ha  and
Anh Quyen Pham
Hung Manh La*
Affiliation:
Advanced Robotics and Automation (ARA) Lab, Department of Computer Science and Engineering, University of Nevada, Reno, USA. E-mails: hla@unr.edu, nhan.ph@nevada.unr.edu, phamquyenanh.qb@gmail.com
Tran Hiep Dinh
Affiliation:
School of Electrical, Mechanical and Mechatronic Systems, University of Technology Sydney, Australia. E-mails: TranHiep.Dinh@student.uts.edu.au, quang.ha@uts.edu.au
Nhan Huu Pham
Affiliation:
Advanced Robotics and Automation (ARA) Lab, Department of Computer Science and Engineering, University of Nevada, Reno, USA. E-mails: hla@unr.edu, nhan.ph@nevada.unr.edu, phamquyenanh.qb@gmail.com
Quang Phuc Ha
Affiliation:
School of Electrical, Mechanical and Mechatronic Systems, University of Technology Sydney, Australia. E-mails: TranHiep.Dinh@student.uts.edu.au, quang.ha@uts.edu.au
Anh Quyen Pham
Affiliation:
Advanced Robotics and Automation (ARA) Lab, Department of Computer Science and Engineering, University of Nevada, Reno, USA. E-mails: hla@unr.edu, nhan.ph@nevada.unr.edu, phamquyenanh.qb@gmail.com
*
*Corresponding author. E-mail: hla@unr.edu
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Summary

This paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.

Keywords

Field roboticsClimbing robotsSteel bridge inspectionImage stitching3D map constructionSteel crackHistogram thresholdingImage segmentation

Information

Type
Articles
Information
Robotica , Volume 37 , Issue 5 , May 2019 , pp. 947 - 967
Copyright
Copyright © Cambridge University Press 2018 

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