S.A.R.A.H.: The Bipedal Robot with Machine Learning Step Decision Making

Christos Kouppas1, Qinggang Meng 1, Mark King 2, and Dennis Majoe3
1. Computer Science, Loughborough University, Loughborough, UK
2. Sports Biomechanics, Loughborough University, Loughborough, UK
3. Computer Science, ETH Zürich, Zürich, Switzerland
Abstract—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.

Index Terms—Decision tree, Linear/Quadratic Discriminant, SVM, KNN, Neural Networks, Bipedal Robot, LSTM

Cite: Christos Kouppas, Qinggang Meng, Mark King, and Dennis Majoe, "S.A.R.A.H.: The Bipedal Robot with Machine Learning Step Decision Making," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 4, pp. 379-384, July 2018. DOI: 10.18178/ijmerr.7.4.379-384
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