Title: Review on reliability in pipeline robotic control systems

Authors: Yun Du; Quanmin Zhu; Sabir Ghauri; Jianhua Zhai; Fanhua Meng; Yongchun Liang

Addresses: Department of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China ' Department of Engineering Design and Mathematics, University of the West of England, Bristol, BS16 1QY, UK ' Department of Engineering Design and Mathematics, University of the West of England, Bristol, BS16 1QY, UK ' Department of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050054, China ' Department of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050054, China ' Department of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050054, China

Abstract: With the increasing use of the pipeline transport facilities, the safety and efficiency of the pipeline systems are very important to energy security. Almost all traditional pipeline inspection methods have shortcomings in one way or another, such as heavy workload and low accuracy. Pipeline robot, effective detection equipment, can do detection work in the narrow place beyond human reach. But to the overall technical level, the research still stays in development stage, far away from massive practical applications because of the reliability issues. This paper reviews the significance and prospects, and representative pipeline robots developed at home and abroad. On the basis of the survey of various locomotion modes, the popular methods used in reliability design such as neural network and reinforcement learning are commented for the future main development.

Keywords: reliability design; pipeline robots; robot control; locomotion mode; neural networks; reinforcement learning; robot locomotion; pipeline transport; pipeline inspection; robotic inspection.

DOI: 10.1504/IJCAT.2014.059092

International Journal of Computer Applications in Technology, 2014 Vol.49 No.1, pp.12 - 21

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 03 Feb 2014 *

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