Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model

The efficiency of helical locomotion in snake-like robots along high-voltage transmission lines is often hindered by low motion efficiency, high joint signal noise, and challenges in traversing obstacles. This study aims to address these issues by proposing a gait generation method that leverages a...

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Main Authors: Zhiyong Yang, Zhen Fang, Shengze Yang, Yuhong Xiong, Daode Zhang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/3/1285
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author Zhiyong Yang
Zhen Fang
Shengze Yang
Yuhong Xiong
Daode Zhang
author_facet Zhiyong Yang
Zhen Fang
Shengze Yang
Yuhong Xiong
Daode Zhang
author_sort Zhiyong Yang
collection DOAJ
description The efficiency of helical locomotion in snake-like robots along high-voltage transmission lines is often hindered by low motion efficiency, high joint signal noise, and challenges in traversing obstacles. This study aims to address these issues by proposing a gait generation method that leverages a standardized Central Pattern Generator (CPG). We modify the traditional Hopf-CPG model by incorporating constraint functions and a frequency-tuning mechanism to regulate the oscillator, which allows for the generation of asymmetric waveform signals for deflection joints and facilitates rapid convergence. The method begins by determining initial and obstacle-crossing state parameters, such as deflection angles and helical radii of the snake-like robot, using the backbone curve method and the Frenet–Serret framework. Subsequently, a CPG neural network is constructed based on Hopf oscillators, with a limit cycle convergent speed adjustment factor and amplitude bias signals to establish a fully connected matrix model for calculating multi-joint output signals. Simulation analysis using Simulink–CoppeliaSim evaluates the robot’s obstacle-crossing ability and the optimization of deflection joint signal noise. The results indicate a 55.70% increase in the robot’s average speed during cable traversal, a 57.53% reduction in deflection joint noise disturbance, and successful crossing of the vibration damper. This gait generation method significantly enhances locomotion efficiency and noise suppression in snake-like robots, offering substantial advantages over traditional approaches.
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issn 2076-3417
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spelling doaj-art-e3509456cebe4b78922488aaf0c991242025-08-20T02:48:06ZengMDPI AGApplied Sciences2076-34172025-01-01153128510.3390/app15031285Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs ModelZhiyong Yang0Zhen Fang1Shengze Yang2Yuhong Xiong3Daode Zhang4Engineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, ChinaEngineering Research and Design Institute of Agricultural Equipment, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaSchool of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, ChinaThe efficiency of helical locomotion in snake-like robots along high-voltage transmission lines is often hindered by low motion efficiency, high joint signal noise, and challenges in traversing obstacles. This study aims to address these issues by proposing a gait generation method that leverages a standardized Central Pattern Generator (CPG). We modify the traditional Hopf-CPG model by incorporating constraint functions and a frequency-tuning mechanism to regulate the oscillator, which allows for the generation of asymmetric waveform signals for deflection joints and facilitates rapid convergence. The method begins by determining initial and obstacle-crossing state parameters, such as deflection angles and helical radii of the snake-like robot, using the backbone curve method and the Frenet–Serret framework. Subsequently, a CPG neural network is constructed based on Hopf oscillators, with a limit cycle convergent speed adjustment factor and amplitude bias signals to establish a fully connected matrix model for calculating multi-joint output signals. Simulation analysis using Simulink–CoppeliaSim evaluates the robot’s obstacle-crossing ability and the optimization of deflection joint signal noise. The results indicate a 55.70% increase in the robot’s average speed during cable traversal, a 57.53% reduction in deflection joint noise disturbance, and successful crossing of the vibration damper. This gait generation method significantly enhances locomotion efficiency and noise suppression in snake-like robots, offering substantial advantages over traditional approaches.https://www.mdpi.com/2076-3417/15/3/1285high-voltage transmission linesnake-like robothelical locomotion gaitCPGobstacle traversal
spellingShingle Zhiyong Yang
Zhen Fang
Shengze Yang
Yuhong Xiong
Daode Zhang
Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
Applied Sciences
high-voltage transmission line
snake-like robot
helical locomotion gait
CPG
obstacle traversal
title Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
title_full Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
title_fullStr Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
title_full_unstemmed Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
title_short Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
title_sort research on the spiral rolling gait of high voltage power line serpentine robots based on improved hopf cpgs model
topic high-voltage transmission line
snake-like robot
helical locomotion gait
CPG
obstacle traversal
url https://www.mdpi.com/2076-3417/15/3/1285
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