Asker, A orcid.org/0000-0002-0121-1227, Xie, S orcid.org/0000-0003-2641-2620 and Dehghani-Sanij, AA (2021) Multi-objective optimization of Force Transmission Quality and Joint Misalignment of a 5-Bar Knee Exoskeleton. In: 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 12-16 Jul 2021, Delft, Netherlands. IEEE , pp. 122-127. ISBN 978-1-6654-4140-7
Abstract
The ability to follow a normal anatomical motion is one of the fundamental design requirements of wearable exoskeleton. Human knee motion is a combination of sliding and rolling actions, which can not be replicated by a simple kinematic pair with a fixed rotation axis. In this paper, a polycentric robotic knee exoskeleton is designed based on the 5-bar mechanism (5- BM) to reproduce knee motion. The Genetic Algorithm finds the optimum parameters of the 5-BM by minimizing a weighted cost function which consists of the average Joint Force Index (JFI) and the misalignment between the centre of rotation of the exoskeleton and the user’s knee. An average and maximum ICR error of 0. 16mm and 0. 43mm is obtained by the optimized 5-BM. It yielded an average actuation toque of 7. 33N.m and JFI of 4.88 compared to 217. 73N.m and 6.04 obtained by the widely used 4-BM.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Oct 2021 10:36 |
Last Modified: | 19 Nov 2021 15:34 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/aim46487.2021.9517444 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179334 |