Author(s)

Xiaomin Shan ¹, Hongyue Peng ²

Affiliation(s)

¹ College of Engineering, Inner Mongolia Minzu University, Tongliao, Inner Mongolia Autonomous Region, China
² Tongliao City Horqin District Agricultural Technology Extension Center, Tongliao City Horqin District Agriculture and Animal Husbandry Bureau, Tongliao, Inner Mongolia Autonomous Region, China

Corresponding Author

Xiaomin Shan

ABSTRACT

Pipelines are the core infrastructure for energy transportation, but their long-distance service and complex internal environment, such as fluid eddies, bends, branches, and sediments, pose significant challenges to the reliability and efficiency of pipeline testing. The traditional pipeline testing methods (manual testing and fixed sensor monitoring) provide low coverage, high labor costs, and adaptability to harsh environments. Although liquid driven pipeline robots do not require external power and are suitable for remote data collection, existing trajectory planning algorithms have not fully considered the dynamic characteristics of the flow field and the complexity of pipeline structures, resulting in high energy consumption, lack of recognition coverage, and poor trajectory adaptability. To address the aforementioned issues, this paper proposes an adaptive recognition route planning algorithm for liquid powered pipeline robots. The experimental results show that the algorithm has good dynamic adaptability, with a coverage rate always above 98% and energy consumption mostly below 80J, effectively improving the adaptability and recognition of liquid powered robots in complex environments.

KEYWORDS

Adaptive Path Planning; Fluid Dynamics Drive; Oil Pipeline Robot; Inspection Coverage Optimization

CITE THIS PAPER

Xiaomin Shan, Hongyue Peng, Adaptive Inspection Path Planning Algorithm for Oil Pipeline Robots Driven by Fluid Kinetic Energy. Journal of Artificial Intelligence Practice (2025) Vol. 8: 9-16. DOI: http://dx.doi.org/10.23977/jaip.2025.080402.

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