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Article: Vision-based Online Learning Kinematic Control for Soft Robots using Local Gaussian Process Regression

TitleVision-based Online Learning Kinematic Control for Soft Robots using Local Gaussian Process Regression
Authors
KeywordsCameras
Robot vision systems
Soft robotics
Kinematics
Robot kinematics
Issue Date2019
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, 2019, v. 4 n. 2, p. 1194-1201 How to Cite?
AbstractSoft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.
Persistent Identifierhttp://hdl.handle.net/10722/272700
ISSN

 

DC FieldValueLanguage
dc.contributor.authorFang, G-
dc.contributor.authorWang, X-
dc.contributor.authorWang, K-
dc.contributor.authorLee, KH-
dc.contributor.authorHo, JD-L-
dc.contributor.authorFu, HC-
dc.contributor.authorFu, DKC-
dc.contributor.authorKwok, KW-
dc.date.accessioned2019-08-06T09:14:53Z-
dc.date.available2019-08-06T09:14:53Z-
dc.date.issued2019-
dc.identifier.citationIEEE Robotics and Automation Letters, 2019, v. 4 n. 2, p. 1194-1201-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/272700-
dc.description.abstractSoft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.-
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©2019 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.subjectCameras-
dc.subjectRobot vision systems-
dc.subjectSoft robotics-
dc.subjectKinematics-
dc.subjectRobot kinematics-
dc.titleVision-based Online Learning Kinematic Control for Soft Robots using Local Gaussian Process Regression-
dc.typeArticle-
dc.identifier.emailLee, KH: brianlkh@HKUCC-COM.hku.hk-
dc.identifier.emailKwok, KW: kwokkw@hku.hk-
dc.identifier.authorityKwok, KW=rp01924-
dc.description.naturepostprint-
dc.identifier.doi10.1109/LRA.2019.2893691-
dc.identifier.scopuseid_2-s2.0-85065930146-
dc.identifier.hkuros300143-
dc.identifier.volume4-
dc.identifier.issue2-
dc.identifier.spage1194-
dc.identifier.epage1201-
dc.publisher.placeUnited States-

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