Wang, R, Hudson, SJ orcid.org/0000-0003-3919-5266, Li, Y et al. (2 more authors) (2020) Normalized Neural Network for Energy Efficient Bipedal Walking Using Nonlinear Inverted Pendulum Model. In: Proceeding of the IEEE International Conference on Robotics and Biomimetics. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 06-08 Dec 2019, Dali, China. IEEE , pp. 1400-1406. ISBN 978-1-7281-6321-5
Abstract
In this paper, we present a novel approach for bipedal walking pattern generation. The proposed method is designed based on 2D inverted pendulum model. All control variables are optimized for an energy efficient gait. To obviate the need of solving non-linear dynamics on-line, a deep neural network is adopted for fast non-linear mapping from desired states to control variables. Normalized dimensionless data is generated to train the neural network, therefore, the trained neural network can be applied to bipedal robots of any size, without any specific modification. The proposed method is later verified through numerical simulations. Simulation results demonstrated that the proposed approach can generate feasible walking motions, and regulate robot’s walking velocity successfully. Its disturbance rejection capability was also validated.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. This is an author produced version of a paper published in Proceeding of the IEEE International Conference on Robotics and Biomimetics. 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. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Nov 2019 10:41 |
Last Modified: | 27 Jan 2020 05:05 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ROBIO49542.2019.8961646 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153224 |