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Article: A Survey for Machine Learning-based Control of Continuum Robots
Title | A Survey for Machine Learning-based Control of Continuum Robots |
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Authors | |
Keywords | continuum robots data-driven control inverse kinematics (IK) kinematic/dynamic model-free control learning-based control |
Issue Date | 2021 |
Publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/Robotics_and_AI |
Citation | Frontiers in Robotics and AI, 2021, v. 8, p. article no. 730330 How to Cite? |
Abstract | Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges. |
Persistent Identifier | http://hdl.handle.net/10722/303961 |
ISSN | 2023 Impact Factor: 2.9 2023 SCImago Journal Rankings: 0.809 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, X | - |
dc.contributor.author | LI, Y | - |
dc.contributor.author | Kwok, KW | - |
dc.date.accessioned | 2021-09-23T08:53:15Z | - |
dc.date.available | 2021-09-23T08:53:15Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Frontiers in Robotics and AI, 2021, v. 8, p. article no. 730330 | - |
dc.identifier.issn | 2296-9144 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303961 | - |
dc.description.abstract | Soft continuum robots have been accepted as a promising category of biomedical robots, accredited to the robots’ inherent compliance that makes them safely interact with their surroundings. In its application of minimally invasive surgery, such a continuum concept shares the same view of robotization for conventional endoscopy/laparoscopy. Different from rigid-link robots with accurate analytical kinematics/dynamics, soft robots encounter modeling uncertainties due to intrinsic and extrinsic factors, which would deteriorate the model-based control performances. However, the trade-off between flexibility and controllability of soft manipulators may not be readily optimized but would be demanded for specific kinds of modeling approaches. To this end, data-driven modeling strategies making use of machine learning algorithms would be an encouraging way out for the control of soft continuum robots. In this article, we attempt to overview the current state of kinematic/dynamic model-free control schemes for continuum manipulators, particularly by learning-based means, and discuss their similarities and differences. Perspectives and trends in the development of new control methods are also investigated through the review of existing limitations and challenges. | - |
dc.language | eng | - |
dc.publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/Robotics_and_AI | - |
dc.relation.ispartof | Frontiers in Robotics and AI | - |
dc.rights | This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | continuum robots | - |
dc.subject | data-driven control | - |
dc.subject | inverse kinematics (IK) | - |
dc.subject | kinematic/dynamic model-free control | - |
dc.subject | learning-based control | - |
dc.title | A Survey for Machine Learning-based Control of Continuum Robots | - |
dc.type | Article | - |
dc.identifier.email | Wang, X: wangxmei@connect.hku.hk | - |
dc.identifier.email | Kwok, KW: kwokkw@hku.hk | - |
dc.identifier.authority | Kwok, KW=rp01924 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3389/frobt.2021.730330 | - |
dc.identifier.scopus | eid_2-s2.0-85117519141 | - |
dc.identifier.hkuros | 324936 | - |
dc.identifier.volume | 8 | - |
dc.identifier.spage | article no. 730330 | - |
dc.identifier.epage | article no. 730330 | - |
dc.identifier.isi | WOS:000709452000001 | - |
dc.publisher.place | Switzerland | - |