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- Publisher Website: 10.1109/ICCAR.2016.7486707
- Scopus: eid_2-s2.0-84978543357
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Conference Paper: Learning the inverse kinematics of tendon-driven soft manipulators with K-nearest Neighbors Regression and Gaussian Mixture Regression
Title | Learning the inverse kinematics of tendon-driven soft manipulators with K-nearest Neighbors Regression and Gaussian Mixture Regression |
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Authors | |
Keywords | Gaussian mixture regression human robot interaction inverse kinematic model K-nearest neighbors regression machine learning surgical manipulators |
Issue Date | 2016 |
Publisher | IEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7481073 |
Citation | Proceedings of 2016 The 2nd International Conference on Control, Automation and Robotics (ICCAR), Hong Kong, 28-30 April 2016, p. 103-107 How to Cite? |
Abstract | Due to the urgent need for Minimally Invasive Surgeries (MIS), all kinds of surgical robots have been developed and investigated intensively in last decades, which can both release the fatigues of surgeons and speed up the process of wound healing. Tendon-Driven Serpentine Manipulator (TSM) maybe among the most widely adopted and promising ones to turn robot assisted MIS into reality. But due to the high nonlinearities and model uncertainties in the TSM system, it is extremely difficult to precisely control the robot. In this paper, we develop and investigate two approaches from machine learning domain, Gaussian Mixture Regression (GMR) and K-Nearest-Neighbors Regression (KNNR), to learn the Inverse Kinematic (IK) model of our TSM robot. Then we compare the performance of GMR and KNNR with that of an IK model derived in previous literatures. Experimental results conducted on a real world TSM robot performing trajectory tracking tasks validate the superior performance of the proposed methods over traditional analytical IK models. |
Description | Session 1: Design & Development of Robot |
Persistent Identifier | http://hdl.handle.net/10722/241688 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chen, J | - |
dc.contributor.author | Lau, HYK | - |
dc.date.accessioned | 2017-06-20T01:47:11Z | - |
dc.date.available | 2017-06-20T01:47:11Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Proceedings of 2016 The 2nd International Conference on Control, Automation and Robotics (ICCAR), Hong Kong, 28-30 April 2016, p. 103-107 | - |
dc.identifier.isbn | 9781467398589 | - |
dc.identifier.uri | http://hdl.handle.net/10722/241688 | - |
dc.description | Session 1: Design & Development of Robot | - |
dc.description.abstract | Due to the urgent need for Minimally Invasive Surgeries (MIS), all kinds of surgical robots have been developed and investigated intensively in last decades, which can both release the fatigues of surgeons and speed up the process of wound healing. Tendon-Driven Serpentine Manipulator (TSM) maybe among the most widely adopted and promising ones to turn robot assisted MIS into reality. But due to the high nonlinearities and model uncertainties in the TSM system, it is extremely difficult to precisely control the robot. In this paper, we develop and investigate two approaches from machine learning domain, Gaussian Mixture Regression (GMR) and K-Nearest-Neighbors Regression (KNNR), to learn the Inverse Kinematic (IK) model of our TSM robot. Then we compare the performance of GMR and KNNR with that of an IK model derived in previous literatures. Experimental results conducted on a real world TSM robot performing trajectory tracking tasks validate the superior performance of the proposed methods over traditional analytical IK models. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7481073 | - |
dc.relation.ispartof | International Conference on Control, Automation and Robotics (ICCAR) | - |
dc.rights | International Conference on Control, Automation and Robotics (ICCAR). Copyright © IEEE. | - |
dc.rights | ©2016 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.subject | Gaussian mixture regression | - |
dc.subject | human robot interaction | - |
dc.subject | inverse kinematic model | - |
dc.subject | K-nearest neighbors regression | - |
dc.subject | machine learning | - |
dc.subject | surgical manipulators | - |
dc.title | Learning the inverse kinematics of tendon-driven soft manipulators with K-nearest Neighbors Regression and Gaussian Mixture Regression | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lau, HYK: hyklau@hkucc.hku.hk | - |
dc.identifier.authority | Lau, HYK=rp00137 | - |
dc.identifier.doi | 10.1109/ICCAR.2016.7486707 | - |
dc.identifier.scopus | eid_2-s2.0-84978543357 | - |
dc.identifier.hkuros | 272857 | - |
dc.identifier.spage | 103 | - |
dc.identifier.epage | 107 | - |
dc.publisher.place | United States | - |