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Landmark detection methods for in-pipe robot traveling in urban gas pipelines

Published online by Cambridge University Press:  09 July 2014

Dong-Hyuk Lee ,
Hyungpil Moon  and
Hyouk Ryeol Choi
Dong-Hyuk Lee
Affiliation:
School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
Hyungpil Moon
Affiliation:
School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
Hyouk Ryeol Choi*
Affiliation:
School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, South Korea
*
*Corresponding author. E-mail: hrchoi@me.skku.ac.kr
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Summary

Elbows and branches in pipelines produce unique image patterns, which can be used as landmarks for autonomous navigation inside the pipelines. This paper presents two landmark detection methods, known as shadow-based method and laser projection method. The first method uses the landmark's unique patterns of shadow produced by the robot's illuminator. The other method exploits special line features generated by its own line-laser beam projector. The basic algorithms for extracting the landmarks are given and special sensor mechanisms are addressed respectively. Finally, the detection performances of each method are validated in various pipeline conditions by using an in-pipe robot, called MRINSPECT-V.

Keywords

In-pipe robotLandmarkPipe inspection
Type
Articles
Information
Robotica , Volume 34 , Issue 3 , March 2016 , pp. 601 - 618
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Ryu, H. and Chung, W. K., “Local map-based exploration for mobile robots,” Intell. Serv. Robot. 6 (4), 199–209 (2013).CrossRefGoogle Scholar
2.Wo, P., Kong, L. and Gao, S., “Holography map for home robot: An object-oriented approach,” Intell. Ser. Robot. 5 (3), 147–157 (2012).Google Scholar
3.Duran, O., Althoefer, K. and Seneviratne, L. D., “Pipe inspection using a laser-based transducer and automated analysis techniques,” IEEE/ASME Trans. Mechatronics 8 (3), 401–409 (2003).CrossRefGoogle Scholar
4.Gomez, F., Althoefer, K. and Seneviratne, L. D., “An Ultrasonic Profiling Method for Sewer Inspection,” Proceedings of the IEEE International Conference Robotics and Automation, New Orleans, LA (Apr. 26–May. 1, 2004) pp. 4858–4863.Google Scholar
5.Park, J., Hyun, D., Cho, W., Kim, T. and Yang, H., “Normal-force control for an in-pipe robot according to the inclination of pipelines,” IEEE Trans. Ind. Electron. 58 (12), 5304–5310 (2011).CrossRefGoogle Scholar
6.Ahrary, A., Tian, L., Kamata, S. and Ishikawa, M., “An Autonomous Sewer Robots Navigation Based on Stereo Camera Information,” Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, Hongkong, China (Nov. 14–16, 2005) pp. 628–633.Google Scholar
7.Nassiraei, A. A. F., Kawamura, Y., Ahrary, A., Mikuriya, Y. and Ishii, K., “Concept and Design of a Fully Autonomous Sewer Pipe Inspection Mobile Robot KANTARO,” Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy (Apr. 10–14, 2007) pp. 136–143.Google Scholar
8.Ahrary, A., Kawamura, Y. and Ishikawa, M., “A Laser Scanner for Landmark Detection with the Sewer Inspection Robot KANTARO,” Proceedings of the International Conference on System of Systems Engineering, Los Angeles, CA (Apr. 24–26, 2006) pp. 310–315.Google Scholar
9.Ahrary, A., Ishikawa, M. and Okada, M., “Experimental Evaluation of Intelligent Fault Detection System for Inspection of Sewer Pipes,” Proceedings of the International Conference on Intelligent Robots and Systems, San Diego, CA (Oct. 29–Nov. 2, 2007) pp. 1248–1253.Google Scholar
10.Kepplin, V., Scholl, K. U. and Berns, K., “A Mechatronic Concept for a Sewer Inspection Robots,” Proceedings of the International Conference on Advanced Intelligent Mechatronics, Atlanta, GA (Sep. 19–23, 1999) pp. 724–729.Google Scholar
11.Kolesnik, M. and Streich, H., “Visual Orientation and Motion Control of MAKRO – Adaptation to the Sewer Environment,” In: Proceedings of the International Conference on Simulation of Adaptive Behavior (MIT Press, Cambridge, MA, 2002) pp. 62–69.Google Scholar
12.Hlavac, V., Jeffery, K. G. and Wiedermann, J., “On Vision-Based Orientation Method of a Robot Head in a Dark Cylindrical Pipe,” Proceedings of the Conference on Current Trends in Theory and Practice of Informatics, Milovy, Czech (Nov. 25–Dec. 2, 2000) pp. 365–374.Google Scholar
13.Rome, E., Surmann, H., Streich, H., Licht, U. and Paap, K., “A Custom IR Scanner for Landmark Detection with the Autonomous Sewer Robot MAKRO,” Proceedings of the International Symposium on Intelligent Robotic Systems, Toulouse, France (Jul. 18–20, 2001) pp. 457–466.Google Scholar
14.Birkenhofer, C., Hoffmeister, J. and Dillmann, R., “Compliant Motion of a Multi-Segmented Inspection Robot,” Proceedings of the International Conference on Intelligent Robots and Systems, Edmonton, Canada (Aug. 2–6, 2005) pp. 3353–3358.Google Scholar
15.Lee, J. S., Roh, S. G., Kim, D. W., Moon, H. P. and Choi, H. R., “In-Pipe Robot Navigation Based on the Landmark Recognition System Using Shadow Images,” Proceeedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan (May 12–17, 2009) pp. 1857–1862.Google Scholar
16.Lee, D. H., Moon, H. P. and Choi, H. R., “Landmark Detection of In-Pipe Working Robot Using Line Laser Beam Projection,” Proceedings of the IEEE International Conference on Control, Automation and Systems, Gyeonggi-do, South Korea (Oct. 27–30, 2010) pp. 611–615.Google Scholar
17.Lee, D. H., Moon, H. P. and Choi, H. R., “Autonomous Navigation of In-Pipe Working Robot in Unknown Pipeline Environment,” Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China (May 9–13, 2011) pp. 1559–1564.Google Scholar
18.Roh, S. G., Kim, D. W., Lee, J. S., Moon, H. P. and Choi, H. R., “Modularized In-Pipe Robot Capable of Selective Navigation Inside Pipelines,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 22–26, 2008) pp. 1724–1729.Google Scholar
19.Kim, D. W., Roh, S. G., Lee, J. S., Lee, S. H. and Choi, H. R., “Development of in-pipe robot using clutch-based selective driving algorithm,” Trans. Korea Soc. Mech. Eng. A 32 (3), 223–231 (2008).CrossRefGoogle Scholar
20.Roh, S. G., Ryew, S. M. and Choi, H. R., “Development of differentially driven in-pipe inspection robot for underground gas pipeline,” Trans. Korea Soc. Mech. Eng. A 25 (12), 2019–2029 (2001).Google Scholar
21.Roh, S. G. and Choi, H. R., “Differential-drive in-pipe robot for moving inside urban gas pipelines,” IEEE Trans. Robot. 21 (1), 1–17 (2005).Google Scholar
22.Hirose, S., Ohno, H., Mitsui, T. and Suyama, K., “Design of In-Pipe Inspection Vehicles for φ25, φ50, φ150 Pipes,” Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI (May 10–15, 1999) pp. 2309–2314.Google Scholar
23.Kirkham, R., Kearney, P. D., Rogers, K. J. and Mashford, J., “PIRAT-A system for quantitative sewer pipe assessment,” Int. J. Robot. Res. 19 (11), 1033–1053 (2000).CrossRefGoogle Scholar
24.Huang, H., Yan, J. and Cheng, T., “Development and fuzzy control of a pipe inspection robot,” IEEE Trans. Ind. Electron. 57 (3), 1088–1095 (2010).CrossRefGoogle Scholar
25.Hu, M. K., “Visual pattern recognition by moment invariants,” IRE Trans. Info. Theory 8 (2), 179–187 (1962).Google Scholar
26.Flusser, J., “On the independence of rotation moment invariants,” Pattern Recognit. 33 (9), 1405–1410 (2000).CrossRefGoogle Scholar
27.Flusser, J. and Suk, T., “Rotation moment invariants for recognition of symmetric objects,” IEEE Trans. Image Proc. 15 (12), 3784–3790 (2006).CrossRefGoogle ScholarPubMed