A minimally designed soft crawling robot for robust locomotion in unstructured pipes.

constant curvature soft crawling robot tendon-driven unstructured pipes

Journal

Bioinspiration & biomimetics
ISSN: 1748-3190
Titre abrégé: Bioinspir Biomim
Pays: England
ID NLM: 101292902

Informations de publication

Date de publication:
08 07 2022
Historique:
received: 27 03 2022
accepted: 30 05 2022
pubmed: 1 6 2022
medline: 9 7 2022
entrez: 31 5 2022
Statut: epublish

Résumé

Soft robots have attracted increasing attention due to their excellent versatility and broad applications. In this article, we present a minimally designed soft crawling robot (SCR) capable of robust locomotion in unstructured pipes with various geometric/material properties and surface topology. In particular, the SCR can squeeze through narrow pipes smaller than its cross section and propel robustly in spiked pipes. The gait pattern and locomotion mechanism of this robot are experimentally investigated and analysed by the finite element analysis, revealing that the resultant forward frictional force is generated due to the asymmetric mechanical properties along the length direction of the robot. The proposed simple yet working SCR could inspire novel designs and applications of soft robots in unstructured narrow canals such as large intestines or industrial pipelines.

Identifiants

pubmed: 35636388
doi: 10.1088/1748-3190/ac7492
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2022 IOP Publishing Ltd.

Auteurs

Wenkai Yu (W)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Xin Li (X)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Dunyu Chen (D)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Jingyi Liu (J)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Jiaji Su (J)

Laboratory for Soft Machines & Electronics, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, United States of America.

Ju Liu (J)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Changyong Cao (C)

Laboratory for Soft Machines & Electronics, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH 44106, United States of America.

Hongyan Yuan (H)

Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing, Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, People's Republic of China.

Articles similaires

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking
Humans Postural Balance Aged Male Female
Humans Female Male Adult Running

Classifications MeSH