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Updated: Sep 22, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
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Twisting for soft intelligent autonomous robot in unstructured environments.

Yao Zhao1, Yinding Chi1, Yaoye Hong1

  • 1Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695.

Proceedings of the National Academy of Sciences of the United States of America
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed twisting soft robots that use environmental thermal energy for autonomous locomotion. These robots navigate complex terrains and overcome obstacles using embodied physical intelligence, without external control.

Keywords:
autonomous soft robotliquid crystal elastomerphysical intelligencesnappingunstructured environment

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Area of Science:

  • Robotics
  • Materials Science
  • Soft Matter Physics

Background:

  • Autonomous locomotion in soft robots is crucial for environmental interaction.
  • Existing soft robots often require external control or complex on-board systems for navigation.
  • Harvesting energy from the environment for self-powered operation remains a significant challenge.

Purpose of the Study:

  • To design and demonstrate soft robots capable of adaptive, intelligent autonomous locomotion in unstructured environments.
  • To achieve self-powered operation by harvesting ambient thermal energy.
  • To enable robots to navigate and overcome obstacles without human intervention or external controls.

Main Methods:

  • Fabrication of soft robots using twisted thermal-responsive liquid crystal elastomer ribbons.
  • Utilizing the inherent physical intelligence of the twisting geometry and snap-through instability.
  • Testing locomotion and obstacle negotiation on various terrains including hard surfaces, granular substrates, and complex obstacle courses.
  • Employing theoretical modeling and finite element simulations to understand the underlying physical mechanisms.

Main Results:

  • The soft robots successfully harvested thermal energy for locomotion on diverse surfaces, including loose sand and rocks.
  • Robots demonstrated adaptive navigation, including ascending slopes, crossing ripples, and escaping burial.
  • The twisting design enabled anchoring in loose sand.
  • Obstacle negotiation was achieved through self-turning and self-snapping mechanisms, including escaping confined spaces and mazes.

Conclusions:

  • Twisting soft robots with embodied physical intelligence offer a novel approach to autonomous locomotion.
  • Harnessing snap-through instability and twisting geometry provides a robust mechanism for adaptive robot-environment interaction.
  • This design eliminates the need for on-board or external controls, paving the way for truly autonomous soft robots.