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Article: Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping

TitleLearning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
Authors
KeywordsGrasping
optical fiber
tactile sensing
soft robotics
Issue Date2021
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE
Citation
IEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3817-3824 How to Cite?
AbstractThis letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY .
Persistent Identifierhttp://hdl.handle.net/10722/300646
ISSN
2020 Impact Factor: 3.741
2020 SCImago Journal Rankings: 1.123
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYANG, L-
dc.contributor.authorHan, X-
dc.contributor.authorGuo, W-
dc.contributor.authorWan, F-
dc.contributor.authorPan, J-
dc.contributor.authorSong, C-
dc.date.accessioned2021-06-18T14:54:58Z-
dc.date.available2021-06-18T14:54:58Z-
dc.date.issued2021-
dc.identifier.citationIEEE Robotics and Automation Letters, 2021, v. 6 n. 2, p. 3817-3824-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/300646-
dc.description.abstractThis letter presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness. Video: https://www.youtube.com/watch?v=ynCfSA4FQnY .-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE-
dc.relation.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx 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.-
dc.subjectGrasping-
dc.subjectoptical fiber-
dc.subjecttactile sensing-
dc.subjectsoft robotics-
dc.titleLearning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping-
dc.typeArticle-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LRA.2021.3065186-
dc.identifier.scopuseid_2-s2.0-85102714985-
dc.identifier.hkuros323039-
dc.identifier.volume6-
dc.identifier.issue2-
dc.identifier.spage3817-
dc.identifier.epage3824-
dc.identifier.isiWOS:000637531200003-
dc.publisher.placeUnited States-

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