Multi-gait snake robot for inspecting inner wall of a pipeline

Abstract

In the field of pipeline inner wall inspection, the snake robot demonstrates significant advantages over other inspection methods. While a simple traveling wave or meandering motion will suffice for inspecting the inner wall of small-diameter pipes, comprehensively and meticulously inspecting the inner wall of large-diameter pipes requires the snake robot to adopt a helical gait that closely adheres to the inner wall. Our review of existing literature indicates that most research and development on the helical gait of snake robots has focused on the outer surface of cylinders, with very few studies dedicated to developing a helical gait specifically for the inspection of the inner wall of pipes. Therefore, in this study, we propose a helical gait that is suitable for the inner wall of pipes and meets the requirements of gas pipeline engineering. The helical gait is designed using the backbone curve method. First, we create a mathematical model for a circular helix curve with constant curvature and torsion, ensuring it is applicable to a snake robot prototype in a laboratory environment. Subsequently, we calculate the joint angles required for two conical spiral curves with variable curvature and torsion, establish a new model, and define the physical significance of the specific parameters. To ensure the feasibility of the proposed gait, we conduct experiments involving meandering and traveling wave motions to verify the communication and control between the host computer and the snake robot. Building upon this foundation, we further validate the mathematical model of the complex helical motion gait through simulation experiments. Our findings provide a theoretical basis for realizing helical movement with a real snake robot.

Keywords

Pipeline inner wall detection / Snake robot / Spiral motion / Backbone curve / Modeling and simulation

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was suported by the BUCEA Post Graduate Innovation Project, China (PG2023096).

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