Abstract

Magnetic continuum soft robots can actively steer their tip under an external magnetic field, enabling them to effectively navigate in complex in vivo environments and perform minimally invasive interventions. However, the geometries and functionalities of these robotic tools are limited by the inner diameter of the supporting catheter as well as the natural orifices and access ports of the human body. Here, we present a class of magnetic soft-robotic chains (MaSoChains) that can self-fold into large assemblies with stable configurations using a combination of elastic and magnetic energies. By pushing and pulling the MaSoChain relative to its catheter sheath, repeated assembly and disassembly with programmable shapes and functions are achieved. MaSoChains are compatible with state-of-the-art magnetic navigation technologies and provide many desirable features and functions that are difficult to realize through existing surgical tools. This strategy can be further customized and implemented for a wide spectrum of tools for minimally invasive interventions.

Minimally invasive surgeries call for surgical tools that can work at the mesoscale. Here, Gu et al. present a class of magnetic soft robotic chains that can self fold into large assemblies with stable configurations using a combination of elastic and magnetic energies stored in printed chain material.

Details

Title
Self-folding soft-robotic chains with reconfigurable shapes and functionalities
Author
Gu, Hongri 1   VIAFID ORCID Logo  ; Möckli, Marino 2 ; Ehmke, Claas 2   VIAFID ORCID Logo  ; Kim, Minsoo 2   VIAFID ORCID Logo  ; Wieland, Matthias 2 ; Moser, Simon 2 ; Bechinger, Clemens 3   VIAFID ORCID Logo  ; Boehler, Quentin 2   VIAFID ORCID Logo  ; Nelson, Bradley J. 2   VIAFID ORCID Logo 

 ETH Zurich, Institute of Robotics and Intelligent Systems, Zurich, Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780); University of Konstanz, Department of Physics, Konstanz, Germany (GRID:grid.9811.1) (ISNI:0000 0001 0658 7699) 
 ETH Zurich, Institute of Robotics and Intelligent Systems, Zurich, Switzerland (GRID:grid.5801.c) (ISNI:0000 0001 2156 2780) 
 University of Konstanz, Department of Physics, Konstanz, Germany (GRID:grid.9811.1) (ISNI:0000 0001 0658 7699) 
Pages
1263
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2784121227
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.