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In-plane gait planning for earthworm-like metameric robots using genetic algorithm.

Xiong Zhan1, Jian Xu, Hongbin Fang

  • 1School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, People's Republic of China.

Bioinspiration & Biomimetics
|May 30, 2020
PubMed
Summary

This study introduces a new framework for planning the in-plane gaits of earthworm-like robots. The method uses a genetic algorithm to optimize gait parameters for efficient locomotion.

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Area of Science:

  • Robotics
  • Biomimetic Engineering
  • Control Systems

Background:

  • Earthworm-like robots achieve locomotion through periodic shape changes of deformable segments, known as gaits.
  • The hyper-redundant morphology of these robots presents a significant challenge for gait planning.
  • Existing methods primarily focus on rectilinear locomotion, limiting their applicability.

Purpose of the Study:

  • To establish a comprehensive framework for in-plane gait planning in earthworm-like robots.
  • To address the complexity of gait planning for hyper-redundant robotic systems.
  • To enable more versatile locomotion capabilities beyond simple rectilinear movement.

Main Methods:

  • A generic model of earthworm-like robots is reviewed.
  • In-plane gaits are parameterized using the principle of retrograde peristaltic waves.

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  • A framework integrating a locomotion simulation module and a genetic algorithm module is developed for gait parameter optimization.
  • Fitness is evaluated by minimizing locomotion distance and step count, with proposed stopping criteria for efficiency.

    • Main Results:

    • The proposed framework successfully plans in-plane gaits for earthworm-like robots.
    • Gait planning is reduced to optimizing gait parameters through a genetic algorithm.
    • The framework demonstrates improved efficiency in gait evaluation using novel stopping criteria.

    Conclusions:

    • The developed framework advances the state-of-the-art in earthworm-like robot locomotion control.
    • It offers a robust solution for planning complex in-plane gaits, surpassing previous limitations.
    • This research paves the way for more adaptable and efficient robotic locomotion in diverse environments.