A Review of the Current Research Status and Intelligent Development of Small-Diameter Natural Gas Pipeline Inspection Robots
DOI:
https://doi.org/10.63313/AJET.9046Keywords:
Small-Diameter Pipelines, Inspection Robots, Deep Learning, Intelligent DevelopmentAbstract
For small-diameter natural gas pipelines widely distributed in gathering and transportation networks, the confined space, frequent bends, and complex branch configurations pose significant challenges to routine inspection and safe operation and maintenance. This paper presents a comprehensive review of the current state of research on inspection robots for small-diameter pipelines and examines the unique constraints imposed by ultra-narrow environments on robotic structural design and intelligent perception. First, through a comparative analysis of mainstream locomotion mechanisms-including wheeled, tracked, and helical drives-it is identified that adaptive structures featuring active diameter-adjustment capabilities and modular design are essential for enhancing a robot’s flexibility and compliance when navigating bends. Second, the integrated application of detection techniques such as magnetic flux leakage, ultrasonic testing, and visual inspection in small-diameter environments is discussed, with particular emphasis on the advantages and limitations of machine vision in real-time monitoring. Finally, addressing the limitations of embedded computational resources, this study explores the development trends of pipeline inspection technologies toward lightweight, autonomous, and intelligent systems, in conjunction with lightweight deep learning algorithms. The findings indicate that constructing intelligent systems with multimodal sensing capabilities and edge computing performance is a key pathway to achieving proactive, full-lifecycle operation and maintenance of small-diameter pipelines.
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