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Machine learning comparison for step decision making of a bipedal robot

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conference contribution
posted on 05.10.2018 by Christos Kouppas, Qinggang Meng, Mark King, Dennis Majoe
This paper presents the results of several machine learning techniques for step decision in a bipedal robot. The custom developed bipedal robot does not utilize electric motors as actuators and as a result has the disadvantage of imprecise movements. The robot is inherently unstable and maintain its stability by making steps. The classifiers had to learn when and which leg must be moved in order to maintain stability and locomotion. Methods like: Decision tree, Linear/Quadratic Discriminant, SVM, KNN and Neural Networks were trained. The results of their performance/accuracy are noted.

Funding

The project is partially funded from Innovate UK's scheme “Emerging and Enabling Technologies” and the “Engineering and Physical Sciences Research Council” (EPSRC) of UK. We thank, also, Motion Robotics LTD, a company based in Southampton, for the collaboration on the robot design and prototype.

History

School

  • Sport, Exercise and Health Sciences

Published in

2018 3rd International Conference on Control and Robotics Engineering, ICCRE 2018

Pages

21 - 25

Citation

KOUPPAS, C. ... et al, 2018. Machine learning comparison for step decision making of a bipedal robot. Presented at the 2018 3rd International Conference on Control and Robotics Engineering (ICCRE), Nagoya, Japan, 20-23 April 2018, pp.21-25.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2018

Notes

© 2018 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.

ISBN

9781538666630

Language

en

Exports