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Snake-Like Robot Workspace Solving Method Based on Improved Monte Carlo Method.

ZhiYong Yang1, Wang Tian1, HaoYang Wang1

  • 1Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, Hubei 430068, China.

Applied Bionics and Biomechanics
|April 14, 2025
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Summary
This summary is machine-generated.

This study introduces an improved Monte Carlo method for mapping snake-like robot workspaces around power lines. The new approach enhances point distribution and boundary extraction for accurate obstacle avoidance.

Keywords:
Monte Carlo methodsnake-like robotvolume calculationworkspaceα-shape

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Area of Science:

  • Robotics
  • Computational Geometry
  • Mechanical Engineering

Background:

  • Snake-like robots are increasingly used for infrastructure inspection, particularly on high-voltage transmission cables.
  • Traditional workspace determination methods face challenges with point cloud boundary extraction and accuracy.
  • Obstacle avoidance is critical for safe and efficient robot operation in complex environments.

Purpose of the Study:

  • To develop an improved Monte Carlo method for accurately determining the obstacle avoidance workspace of snake-like robots operating on high-voltage transmission cables.
  • To address limitations of traditional Monte Carlo methods, including uneven point distribution and difficulties in boundary extraction.
  • To enhance the precision of workspace volume calculation for improved robot navigation.

Main Methods:

  • An improved Monte Carlo method is proposed, starting with a seed workspace generated by the traditional Monte Carlo method.
  • The seed workspace is enveloped, divided into smaller cubes, and then extended using Gaussian distribution probability density functions.
  • The alpha-shape algorithm is employed to extract point cloud boundaries and accurately calculate the workspace volume.

Main Results:

  • The improved method effectively addresses issues of uneven point distribution and boundary extraction difficulties.
  • Gaussian distribution sampling enhances the generation of the robot's workspace.
  • The alpha-shape algorithm accurately reconstructs the workspace boundaries, validated by simulation experiments.

Conclusions:

  • The proposed improved Monte Carlo method, combined with the alpha-shape algorithm, provides an accurate and reliable approach for determining snake-like robot workspaces.
  • This method significantly improves upon traditional techniques, offering better precision for obstacle avoidance in power line inspection scenarios.
  • The accurate workspace determination is crucial for enhancing the safety and efficiency of snake-like robots in challenging industrial applications.