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Simultaneous Localization and Mapping Methods for Snake-like Robots Based on Gait Adjustment.

Chaoquan Tang1, Zhipeng Zhang1, Meng Sun1

  • 1Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China.

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|November 26, 2024
PubMed
Summary
This summary is machine-generated.

This study enhances snake robot localization and mapping (SLAM) by improving gait motion and fusing visual-inertial data. The new methods reduce sensor interference, improving positioning and map-building accuracy for field applications.

Keywords:
IMUSLAMsnake robotstability controltypical gait

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Snake robots need accurate autonomous localization and mapping (SLAM) for field operations.
  • Their complex motion, including large turns and fast rotations, causes SLAM errors like drift and failure.
  • Head-shaking in snake robots particularly hinders SLAM algorithm convergence.

Purpose of the Study:

  • To develop improved gait motion and sensor fusion methods for robust snake robot SLAM.
  • To address the challenges posed by snake robot locomotion on positioning and map-building accuracy.
  • To enhance the reliability and performance of SLAM systems in challenging robotic applications.

Main Methods:

  • Evaluated the suitability of various snake robot gaits for SLAM.
  • Developed a head stability control method for the serpentine gait to minimize sensor interference.
  • Proposed a tightly coupled visual-inertial SLAM approach integrating serpentine and Arc-Rolling gaits.

Main Results:

  • The improved serpentine gait and head stability control reduced motion-induced sensor interference.
  • The visual-inertial SLAM method demonstrated enhanced robustness and accuracy for snake robots.
  • Experimental validation confirmed significant improvements in positioning and map-building capabilities.

Conclusions:

  • The proposed gait improvement and tightly coupled visual-inertial SLAM effectively enhance snake robot autonomous navigation.
  • These methods mitigate motion artifacts, leading to more reliable and precise localization and mapping.
  • The findings contribute to advancing the field applicability of snake robots.