Embedded shape morphing for morphologically adaptive robots
Jiefeng Sun (),
Elisha Lerner,
Brandon Tighe,
Clint Middlemist and
Jianguo Zhao ()
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Jiefeng Sun: Colorado State University
Elisha Lerner: Colorado State University
Brandon Tighe: Colorado State University
Clint Middlemist: Colorado State University
Jianguo Zhao: Colorado State University
Nature Communications, 2023, vol. 14, issue 1, 1-13
Abstract:
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.
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41708-6
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DOI: 10.1038/s41467-023-41708-6
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