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Embedded shape morphing for morphologically adaptive robots

Author

Listed:
  • Jiefeng Sun

    (Colorado State University
    Yale University)

  • Elisha Lerner

    (Colorado State University)

  • Brandon Tighe

    (Colorado State University)

  • Clint Middlemist

    (Colorado State University)

  • Jianguo Zhao

    (Colorado State University)

Abstract

Shape-morphing robots can change their morphology to fulfill different tasks in varying environments, but existing shape-morphing capability is not embedded in a robot’s body, requiring bulky supporting equipment. Here, we report an embedded shape-morphing scheme with the shape actuation, sensing, and locking, all embedded in a robot’s body. We showcase this embedded scheme using three morphing robotic systems: 1) self-sensing shape-morphing grippers that can adapt to objects for adaptive grasping; 2) a quadrupedal robot that can morph its body shape for different terrestrial locomotion modes (walk, crawl, or horizontal climb); 3) an untethered robot that can morph its limbs’ shape for amphibious locomotion. We also create a library of embedded morphing modules to demonstrate the versatile programmable shapes (e.g., torsion, 3D bending, surface morphing, etc.). Our embedded morphing scheme offers a promising avenue for robots to reconfigure their morphology in an embedded manner that can adapt to different environments on demand.

Suggested Citation

  • Jiefeng Sun & Elisha Lerner & Brandon Tighe & Clint Middlemist & Jianguo Zhao, 2023. "Embedded shape morphing for morphologically adaptive robots," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41708-6
    DOI: 10.1038/s41467-023-41708-6
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    References listed on IDEAS

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