Martin, J, Scaglioni, B orcid.org/0000-0003-4891-8411, Norton, J orcid.org/0000-0001-9981-5936 et al. (2 more authors) (2019) Toward Autonomous Robotic Colonoscopy: Motion Strategies for Magnetic Capsule Navigation. In: 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS). CBS 2018, 25-27 Oct 2018, Shenzen, China. IEEE , pp. 240-244. ISBN 9781538673553
Abstract
In this paper, a set of techniques aimed at autonomously navigating a tethered magnetic capsule for endoluminal inspection of the large bowel is presented. The manual navigation of magnetic capsules for colonoscopy can exhibit several challenges if the full control of the capsule pose is left to the clinician. Tight bends, tissue folds and large diverticula can obstruct the motion of the capsule, yielding the control system to apply greater forces and torques, with no substantial effect. For this reason, a supervisory system, capable of influencing the capsule motion to avoid obstruction and to overcome obstacles is here described. The adopted approach is based on the 'surfing' principle, where the capsule is navigated in such a way to slide on the tissue folds rather than against them. The proposed technique has been validated by means of experiments in a colon phantom, experiments have shown that the adoption of this approach allows the navigation of 350mm in the phantom, while a fully-manual teleoperation of the capsule only reaches a depth of 75mm.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2018, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Colon, Endoscopes, Robots, Force, Navigation, Phantoms, Colonoscopy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society wm150122 EPSRC EP/P51097X/1 National Institute of Health - NIH (PHS) 6R01EB018992 Royal Society CH160052 EPSRC EP/P027938/1 EPSRC ep/k034537/2 NIHR National Inst Health Research Not Known University of Turin Not Known Cancer Research UK C64904/A27744 |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2019 12:52 |
Last Modified: | 15 Mar 2019 12:52 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CBS.2018.8612267 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143461 |