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Curvilinear Kirigami Skins Let Soft Bending Actuators Slither Faster.
Branyan, Callie; Rafsanjani, Ahmad; Bertoldi, Katia; Hatton, Ross L; Mengüç, Yigit.
Afiliação
  • Branyan C; Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR, United States.
  • Rafsanjani A; Sandia National Laboratories, Albuquerque, NM, United States.
  • Bertoldi K; Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Odense, Denmark.
  • Hatton RL; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States.
  • Mengüç Y; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States.
Front Robot AI ; 9: 872007, 2022.
Article em En | MEDLINE | ID: mdl-35592681
ABSTRACT
The locomotion of soft snake robots is dependent on frictional interactions with the environment. Frictional anisotropy is a morphological characteristic of snakeskin that allows snakes to engage selectively with surfaces and generate propulsive forces. The prototypical slithering gait of most snakes is lateral undulation, which requires a significant lateral resistance that is lacking in artificial skins of existing soft snake robots. We designed a set of kirigami lattices with curvilinearly-arranged cuts to take advantage of in-plane rotations of the 3D structures when wrapped around a soft bending actuator. By changing the initial orientation of the scales, the kirigami skin produces high lateral friction upon engagement with surface asperities, with lateral to cranial anisotropic friction ratios above 4. The proposed design increased the overall velocity of the soft snake robot more than fivefold compared to robots without skin.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Robot AI Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Robot AI Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos