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Physically intelligent autonomous soft robotic maze escaper.

Yao Zhao1, Yaoye Hong1, Yanbin Li1

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

Science Advances
|September 8, 2023
PubMed
Summary
This summary is machine-generated.

Soft robots with asymmetric liquid crystal elastomer designs can autonomously navigate complex mazes. These robots utilize geometric and material intelligence for self-powered, self-turning capabilities, enabling escape without computational control.

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Area of Science:

  • Soft robotics
  • Materials science
  • Autonomous systems

Background:

  • Autonomous navigation in unstructured environments is challenging for soft robots.
  • Existing soft robots often require complex computational intelligence and onboard power.
  • Soft robots need novel designs for independent exploration and navigation.

Purpose of the Study:

  • To develop a soft robot capable of autonomous maze navigation without a complex control system.
  • To harness geometric and material properties for intrinsic robotic intelligence.
  • To demonstrate successful autonomous escaping from complex mazes using a self-rolling robot.

Main Methods:

  • Utilizing liquid crystal elastomers for robot construction.
  • Designing asymmetric hybrid twisted and helical shapes for self-turning.
  • Employing environmental thermal energy for robot power.
  • Incorporating self-snapping mechanisms for motion reflection.

Main Results:

  • The asymmetric soft robot demonstrated sustained self-turning capabilities.
  • The robot navigated complex mazes using unique curved zigzag paths.
  • Successful autonomous escaping was achieved in various challenging maze environments.
  • The robot navigated mazes on granular terrains, with narrow gaps, and with changing layouts.

Conclusions:

  • Geometric and material intelligence can enable autonomous robotic behavior.
  • Liquid crystal elastomer soft robots can navigate complex environments without external control.
  • This approach offers a new paradigm for autonomous soft robotics in unstructured settings.