Tunnel structural inspection and assessment using an autonomous robotic system
This paper presents the ROBO-SPECT European FP7 project, funded under the ICT-2013.2.2 programme on Robotics use cases & Accompanying measures, a robotized alternative to manual tunnel structural inspection and assessment of cracks and other defects. Physical developments include the design and...
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| Vydáno v: | Automation in construction Ročník 87; s. 117 - 126 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.03.2018
Elsevier BV |
| Témata: | |
| ISSN: | 0926-5805, 1872-7891 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | This paper presents the ROBO-SPECT European FP7 project, funded under the ICT-2013.2.2 programme on Robotics use cases & Accompanying measures, a robotized alternative to manual tunnel structural inspection and assessment of cracks and other defects. Physical developments include the design and implementation of a multi-degree-of-freedom robotic system, composed by a mobile vehicle, an extended crane, and a high precision robotic arm. A semi-supervised computer vision system to detect tunnel defects, and a ultrasonic sensor (US) robotic tool to measure width and depth of detected cracks have been also developed. An overview of defect detection methods, as well as the fundamental aspects of the project architecture and design, will be presented. In addition, the developed and implemented arm tip positioning algorithm for the US robotic tool shall be detailed. Finally, experimental evidence and details on validation in a real motorway tunnel with ongoing traffic will be provided.
•Development of a mobile robotic system for tunnel lining inspection.•Autonomous inspection of cracks and other defects on tunnels.•The robotic arm is able to safely position the ultrasonic sensors on a crack.•Experiments and demonstrations were successfully performed in a real tunnel with on-going traffic. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0926-5805 1872-7891 |
| DOI: | 10.1016/j.autcon.2017.12.001 |