Smart Pipe Inspection Robot With In-Chassis Motor Actuation Design and Integrated AI-Powered Defect Detection System

被引:0
作者
Zholtayev, Darkhan [1 ]
Dauletiya, Daniyar [1 ]
Tileukulova, Aisulu [2 ]
Akimbay, Dias [3 ]
Nursultan, Manat [3 ]
Bushanov, Yersaiyn [3 ]
Kuzdeuov, Askat [4 ]
Yeshmukhametov, Azamat [3 ,4 ]
机构
[1] Astana IT Univ, Dept Computat & Data Sci, Astana 020000, Kazakhstan
[2] Al Farabi Kazakh Natl Univ, Dept Phys & Technol, Alma Ata 050040, Kazakhstan
[3] Nazarbayev Univ, Sch Engn & Digital Sci, Dept Robot & Mechatron, Astana 010000, Kazakhstan
[4] Nazarbayev Univ, Inst Smart Syst & Artificial Intelligence, Astana 010000, Kazakhstan
关键词
Inspection; Robots; Image edge detection; Simultaneous localization and mapping; Computational modeling; Sensors; Inpipe robot; robot design; machine learning; defect detection; pipe inspection; SLAM; COMPUTER VISION;
D O I
10.1109/ACCESS.2024.3450502
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the contemporary world, inspection operations have become a critical component of infrastructure maintenance. Over the years, the demand for comprehensive inspection of pipes, both internally and externally, has grown increasingly complex and challenging. Consequently, there is a pressing need for significant advancements in in-pipe robots, particularly in the areas of inspection speed, defect detection precision, and overall reliability. Recent developments in new devices and sensors have markedly improved our capability to inspect and diagnose defects within pipes with greater accuracy. Furthermore, the application of machine learning tools has optimized the inspection process, enhancing the detection and recognition of potential pipe defects, such as rust, blockages, and welding anomalies. This research introduces a novel mobile robot platform specifically designed for pipe inspection. It integrates an advanced machine learning model that effectively detects and identifies key pipe defects, including rust, compromised welding quality, and pipe deformation. Additionally, this platform offers enhancements in inspection speed. The integration of these technologies represents a significant stride in the field of infrastructure maintenance, setting a new standard for efficiency and precision in pipe inspection.
引用
收藏
页码:119520 / 119534
页数:15
相关论文
共 68 条
[1]   Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review [J].
Aitken, Jonathan M. ;
Evans, Mathew H. ;
Worley, Rob ;
Edwards, Sarah ;
Zhang, Rui ;
Dodd, Tony ;
Mihaylova, Lyudmila ;
Anderson, Sean R. .
IEEE ACCESS, 2021, 9 :140173-140198
[2]   Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices [J].
Ali, Owais ;
Ali, Hazrat ;
Shah, Syed Ayaz Ali ;
Shahzad, Aamir .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (11) :4593-4597
[3]  
[Anonymous], 2024, ResNet-50 Trained on ImageNet Competition Data
[4]  
[Anonymous], 2021, Energy Resource Guide-Oil and Gas-Kazakhstan
[5]   Visual inspection and characterization of external corrosion in pipelines using deep neural network [J].
Bastian, Blossom Treesa ;
Jaspreeth, N. ;
Ranjith, S. Kumar ;
Jiji, C. V. .
NDT & E INTERNATIONAL, 2019, 107
[6]   DBSCAN and TD Integrated Wi-Fi Positioning Algorithm [J].
Bi, Jingxue ;
Cao, Hongji ;
Wang, Yunjia ;
Zheng, Guoqiang ;
Liu, Keqiang ;
Cheng, Na ;
Zhao, Meiqi .
REMOTE SENSING, 2022, 14 (02)
[7]   Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning [J].
Calton, Landon ;
Wei, Zhangping .
APPLIED SCIENCES-BASEL, 2022, 12 (03)
[8]   A Generalized Density-Based Algorithm for the Spatiotemporal Tracking of Drought Events [J].
Cammalleri, C. ;
Toreti, A. .
JOURNAL OF HYDROMETEOROLOGY, 2023, 24 (03) :537-548
[9]   Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm [J].
Cheng, Fang ;
Niu, Guofeng ;
Zhang, Zhizhong ;
Hou, Chengjie .
SENSORS, 2022, 22 (23)
[10]   Learning and SLAM Based Decision Support Platform for Sewer Inspection [J].
Chuang, Tzu-Yi ;
Sung, Cheng-Che .
REMOTE SENSING, 2020, 12 (06)