On planar self-folding magnetic chains: Comparison of Newton-Euler dynamics and internal energy optimisation

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dc.contributor.author Fass, T. H.
dc.contributor.author Hao, Guangbo
dc.contributor.author Cantillon-Murphy, Pádraig
dc.date.accessioned 2020-09-11T08:29:35Z
dc.date.available 2020-09-11T08:29:35Z
dc.date.issued 2020-07-16
dc.identifier.citation Fass, T. H., Hao, G. and Cantillon-Murphy, P. (2020) 'On planar self-folding magnetic chains: Comparison of Newton-Euler dynamics and internal energy optimisation', Robotics and Autonomous Systems, 132, 103601 (16pp). doi: 10.1016/j.robot.2020.103601 en
dc.identifier.volume 132 en
dc.identifier.startpage 1 en
dc.identifier.endpage 16 en
dc.identifier.issn 0921-8890
dc.identifier.uri http://hdl.handle.net/10468/10508
dc.identifier.doi 10.1016/j.robot.2020.103601 en
dc.description.abstract Within the wide field of self-assembly, the self-folding chain has the unique capability to pass through narrow openings, too small for the assembled structure, yet consists in one connected body. This paper presents a novel analytical framework and corresponding experimental setup to quantify the results of a self-folding process using magnetic forces at the centimetre-scale, with the aim to put experimental results and prediction methods in the context of surgical anchoring and therapy. Two possibilities to predict the folding of a chain of magnetic components in 2D are compared and investigated in an experimental setup. Folding prediction by system Coulomb energy, neglecting folding dynamics, is compared with a simulation of the system dynamics using a novel approach for 2D folding chains, derived from the Newton-Euler equations. The presented algorithm is designed for the parallel computation architecture of modern computer systems to be easily applicable and to achieve an improved simulation speed. The experimental setup for the self-folding chain used to validate the simulation results consists of a chain of magnetic components where movement is limited to one plane and the chain is agitated by the magnetic forces between the chain components. The folding process of the experimental setup is validated for its stability and predictability under different deployment modes. Finally, the results are discussed in light of the folding prediction of longer chains. The implications of the presented findings for a 3D folding chain are discussed together with the challenges to apply the novel dynamics simulation algorithm to the 3D case. The work clearly demonstrates the potential for this novel approach for complex self-folding applications such as magnetic compression anastomosis and anchoring in minimally invasive surgery. en
dc.description.sponsorship Science Foundation Ireland (17/CDA/4771) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier B.V. en
dc.rights © 2020, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license. en
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Magnetic self-folding chain en
dc.subject Magnetic surgery en
dc.subject Self-assembly en
dc.subject Newton-Euler dynamics en
dc.title On planar self-folding magnetic chains: Comparison of Newton-Euler dynamics and internal energy optimisation en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Guangbo Hao, Electrical & Electronic Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: g.hao@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 24 months after publication by request of the publisher. en
dc.check.date 2022-07-16
dc.date.updated 2020-09-11T08:08:11Z
dc.description.version Accepted Version en
dc.internal.rssid 529690125
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Robotics and Autonomous Systems en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress g.hao@ucc.ie en
dc.identifier.articleid 103601 en


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© 2020, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2020, Elsevier B.V. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.
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