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

Darkhan Zholtayev, Daniyar Dauletiya, Aisulu Tileukulova, Dias Akimbay, Manat Nursultan, Yersaiyn Bushanov, Askat Kuzdeuov, Azamat Yeshmukhametov

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Access
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Computational modeling
  • Defect detection
  • Image edge detection
  • Inpipe robot
  • Inspection
  • Machine learning
  • Machine learning
  • Pipe inspection
  • Robot design
  • Robots
  • Sensors
  • Simultaneous localization and mapping
  • SLAM

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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