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Article: Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

TitleNonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation
Authors
Issue Date2017
PublisherMary Ann Liebert.
Citation
Soft Robotics, 2017, v. 4 n. 4, p. 324-337 How to Cite?
AbstractBioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.
Persistent Identifierhttp://hdl.handle.net/10722/243138
ISSN
2017 Impact Factor: 5.057
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLee, KH-
dc.contributor.authorFu, KCD-
dc.contributor.authorLeong, CWM-
dc.contributor.authorChow, CK-
dc.contributor.authorFu, HC-
dc.contributor.authorAlthoefer, K-
dc.contributor.authorSze, KY-
dc.contributor.authorYeung, CK-
dc.contributor.authorKwok, KW-
dc.date.accessioned2017-08-25T02:50:33Z-
dc.date.available2017-08-25T02:50:33Z-
dc.date.issued2017-
dc.identifier.citationSoft Robotics, 2017, v. 4 n. 4, p. 324-337-
dc.identifier.issn2169-5172-
dc.identifier.urihttp://hdl.handle.net/10722/243138-
dc.description.abstractBioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments.-
dc.languageeng-
dc.publisherMary Ann Liebert.-
dc.relation.ispartofSoft Robotics-
dc.rightsSoft Robotics. Copyright © Mary Ann Liebert.-
dc.rightsFinal publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/[insert DOI]-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleNonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation-
dc.typeArticle-
dc.identifier.emailFu, KCD: dennyfu@hku.hk-
dc.identifier.emailSze, KY: kysze@hku.hk-
dc.identifier.emailKwok, KW: kwokkw@hku.hk-
dc.identifier.authoritySze, KY=rp00171-
dc.identifier.authorityKwok, KW=rp01924-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1089/soro.2016.0065-
dc.identifier.hkuros274303-
dc.identifier.volume4-
dc.identifier.issue4-
dc.identifier.spage324-
dc.identifier.epage337-
dc.identifier.isiWOS:000430702900003-
dc.publisher.placeUS-

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