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S.A.R.A.H.: The bipedal robot with machine learning step decision making

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journal contribution
posted on 07.09.2018 by Christos Kouppas, Qinggang Meng, Mark King, Dennis Majoe
Herein, we describe a custom-made bipedal robot that uses electromagnets for performing movements as opposed to conventional DC motors. The robot uses machine learning to stabilize its self by taking steps. The results of several machine learning techniques for step decision are described. The robot does not use electric motors as actuators. As a result, it makes imprecise movements and is inherently unstable. To maintain stability, it must take steps. Classifiers are required to learn from users about when and which leg to move to maintain stability and locomotion. Classifiers such as Decision tree, Linear/Quadratic Discriminant, Support Vector Machine, K-Nearest Neighbor, and Neural Networks are trained and compared. Their performance/accuracy is noted.

Funding

The project is partially funded from Innovate UK’s scheme “Emerging and Enabling Technologies” and Center of Doctoral Training of Embedded Intelligence (CDT-EI) funded from “Engineering and Physical Sciences Research Council” of UK.

History

School

  • Sport, Exercise and Health Sciences

Published in

International Journal of Mechanical Engineering and Robotics Research

Volume

7

Issue

4

Pages

379 - 384

Citation

KOUPPAS, C. ... et al, 2018. S.A.R.A.H.: The bipedal robot with machine learning step decision making. International Journal of Mechanical Engineering and Robotics Research, 7 (4), pp.379-384.

Publisher

© IJMERR

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

08/07/2018

Publication date

2018-07-01

Notes

This is an Open Access Article. It is published by IJMERR under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND). Full details of this licence are available at: http://creativecommons.org/licenses/by-nc-nd/4.0/

ISSN

2278-0149

Language

en

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