Detection and Control of Flexible Peristaltic Pipeline Robot in Elbow

Abstract:

In order to make the peristaltic pipeline robot go through elbow quickly and efficiently, a multi-sensor information fusion algorithm based on relation matrix and fuzzy logic control is proposed. In this method, relation matrix is adopted to fuse the information from ultrasonic sensors and infrared sensors, while fuzzy neural network is employed to make walking strategy and remove obstructions. Simulation and platform testing are designed to verify the effectiveness of this method. The test results show that this method can significantly improve distance measuring accuracy and walking efficiency.

• Periodical: Applied Mechanics and Materials (Volume 389)
• Pages: 789-795
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.389.789
• Online since: August 2013
• Authors: Xin Xing Zhang, Yan Heng Zhang, Han Xu Sun, Si Cheng Nian
• Keywords: Fuzzy Neural Network (FNN), Incidence Matrix Theory, Multi-Sensor Information Fusion, Pipeline Robot

References

[1] Yoon K. H, Park Y.W. Design, fabrication, and characterization of in-pipe robot with controllable magnetic force . Automation Science and Engineering (CASE), 2012 IEEE International Conference on. Digital Object Identifier: 10. 1109/CoASE. 2012. 6386443. 2012: 786-789.

DOI: 10.1109/coase.2012.6386443

[2] Dong-Hyuk Lee, Hyungpil Moon, Hyouk Ryeol Choi. Autonomous navigation of in-pipe working robot in unknown pipeline environment. Robotics and Automation (ICRA), 2011 IEEE International Conference on Digital Object Identifier. 10. 1109/ICRA. 2011. 5980503. 2011: 1559-1564.

DOI: 10.1109/icra.2011.5980503

[3] Camerini, C, Freitas M, Langer R .A. A robot for offshore pipeline inspection. vonder Weid, J.P. ; Marnet, R; Kubrusly, A.C. Industry Applications (INDUSCON), 2010 9th IEEE/IAS International Conference on Digital Object Identifier: 10. 1109/INDUSCON. 2010. 5740063. 2010: 1-6.

DOI: 10.1109/induscon.2010.5740063

[4] Gulrez T, Kavakli M. Sensor Relevance Establishment Problem in SharedInformation Gathering Sensor Networks. Networking, Sensing and Control, 2007 IEEE International Conference on Digital Object Identifier: 10. 1109/ICNSC. 2007. 372856. 2007: 650-655.

DOI: 10.1109/icnsc.2007.372856

[5] Borenstein J, Koren Y. Obstacle avoidancewith ultrasonic sen-sors[J]. Journal ofRobotics and Automation. 1988, 4(2): 213-218.

[6] LI Junyuan, CHEN Hongjun, ZHANG Xiaohua. Localization Technique of Pipeline Robot Based on Multi-sensor Data Fusion[J]. CONTROL AND DECISION. 2006, 21(6): 661-665.

DOI: 10.1109/icca.2005.1528261

[7] WANG Zhongwei, CAO Qixin, LUAN Nan. Precision Location Technology of Pipeline Robot Based on Multi-Sensor Data Fusion[J]. ROBOT. 2008, 30(3): 238-241.

[8] Li Qingyun, Yang Wenkai, Li Yang. Application of fuzzy neural networks information fusion in the pipeline robot navigation[J]. MICROCOMPUTER & ITS APPLICATIONS. 2011, 30(3): 82-85.

[9] Wang Yaonan, Li Shutao. Multisensor Information Fusion and Its Application: A Survey[J]. CONTROL AND DECISION. 2001, 16(5): 518-522.

[10] LI Qiang, YANG Xiaojing, WEI Lan. Intelligent Robot Based on Neural Networks Information Fusion[J]. MECHANICAL & ELECTRICAL ENGINEERING TECHNOLOGY. 2006, 35(6).

[11] WANG Li, YANG Quansheng. A Method for Data Fusion of Multi-sensor[J]. COMPUTER TECHNOLOGY AND DEVELOPMENT. 2008, 18(2).