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Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure

Macaulay, Michael O., Shafiee, Mahmood (2022) Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure. Autonomous Intelligent Systems, 2 (8). pp. 1-25. E-ISSN 2730-616X. (doi:10.1007/s43684-022-00025-3) (KAR id:94824)

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Abstract

Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry.

Item Type: Article
DOI/Identification number: 10.1007/s43684-022-00025-3
Uncontrolled keywords: Machine learning, Deep learning, Robotics and autonomous system (RAS), Inspection, Monitoring, Mechanical systems, Civil infrastructure
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7885 Computer engineering. Computer hardware
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 07 May 2022 23:31 UTC
Last Modified: 09 May 2022 15:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/94824 (The current URI for this page, for reference purposes)
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
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