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- Publisher Website: 10.1109/ROBIO.2015.7419731
- Scopus: eid_2-s2.0-84964414521
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Conference Paper: A hybrid inverse kinematics framework for redundant robot manipulators based on hierarchical clustering and distal teacher learning
Title | A hybrid inverse kinematics framework for redundant robot manipulators based on hierarchical clustering and distal teacher learning |
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Authors | |
Issue Date | 2016 |
Publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856 |
Citation | IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015, p. 2597-2602 How to Cite? |
Abstract | Inverse kinematic models are among the most significant tools in robotics and a real time framework to solve the inverse kinematics is necessary for the robot to perform required task. However, for redundant robot arms with more degrees of freedom than required for a given task, the inverse kinematics remains a difficult and challenging problem. With the extra degrees of freedom, redundant robot arms can be much more flexible and dexterous than traditional non-redundant manipulators, thus are very suitable for performing many challenging tasks, such as grasping novel objects in flight and conducting human surgeries. In this work, a distal teacher learning framework combined with hierarchical clustering algorithm is proposed to solve the inverse kinematics of redundant robot arms. The hierarchical clustering algorithm is used to learn the inverse kinematic model of the robot, and the prediction error of the learned model is compensated by the distal teacher. Simulations in MATLAB performed on a five-degrees of freedom planar redundant robot have verified the effectiveness and efficiency of this method. |
Persistent Identifier | http://hdl.handle.net/10722/241686 |
DC Field | Value | Language |
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dc.contributor.author | Chen, J | - |
dc.contributor.author | Lau, HYK | - |
dc.date.accessioned | 2017-06-20T01:47:10Z | - |
dc.date.available | 2017-06-20T01:47:10Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015, p. 2597-2602 | - |
dc.identifier.uri | http://hdl.handle.net/10722/241686 | - |
dc.description.abstract | Inverse kinematic models are among the most significant tools in robotics and a real time framework to solve the inverse kinematics is necessary for the robot to perform required task. However, for redundant robot arms with more degrees of freedom than required for a given task, the inverse kinematics remains a difficult and challenging problem. With the extra degrees of freedom, redundant robot arms can be much more flexible and dexterous than traditional non-redundant manipulators, thus are very suitable for performing many challenging tasks, such as grasping novel objects in flight and conducting human surgeries. In this work, a distal teacher learning framework combined with hierarchical clustering algorithm is proposed to solve the inverse kinematics of redundant robot arms. The hierarchical clustering algorithm is used to learn the inverse kinematic model of the robot, and the prediction error of the learned model is compensated by the distal teacher. Simulations in MATLAB performed on a five-degrees of freedom planar redundant robot have verified the effectiveness and efficiency of this method. | - |
dc.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856 | - |
dc.relation.ispartof | IEEE International Conference on Robotics and Biomimetics Proceedings | - |
dc.rights | IEEE International Conference on Robotics and Biomimetics Proceedings. 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.title | A hybrid inverse kinematics framework for redundant robot manipulators based on hierarchical clustering and distal teacher learning | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lau, HYK: hyklau@hkucc.hku.hk | - |
dc.identifier.authority | Lau, HYK=rp00137 | - |
dc.identifier.doi | 10.1109/ROBIO.2015.7419731 | - |
dc.identifier.scopus | eid_2-s2.0-84964414521 | - |
dc.identifier.hkuros | 272855 | - |
dc.identifier.spage | 2597 | - |
dc.identifier.epage | 2602 | - |
dc.publisher.place | United States | - |