Open AccessUse of Support Vector Regression in Stable Trajectory Generation for Walking Humanoid Robots
Author(s) -
Kim Dong Won,
Seo SamJun,
Silva Clarence W.,
Park GwiTae
Publication year - 2009
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.09.0108.0452
Subject(s) - humanoid robot , support vector machine , trajectory , kernel (algebra) , radial basis function , robot , control theory (sociology) , computer science , artificial intelligence , polynomial , polynomial kernel , identification (biology) , function (biology) , kernel method , mathematics , control (management) , artificial neural network , mathematical analysis , physics , botany , combinatorics , astronomy , evolutionary biology , biology
This paper concerns the use of support vector regression (SVR), which is based on the kernel method for learning from examples, in identification of walking robots. To handle complex dynamics in humanoid robot and realize stable walking, this paper develops and implements two types of reference natural motions for a humanoid, namely, walking trajectories on a flat floor and on an ascending slope. Next, SVR is applied to model stable walking motions by considering these actual motions. Three kinds of kernels, namely, linear, polynomial, and radial basis function (RBF), are considered, and the results from these kernels are compared and evaluated. The results show that the SVR approach works well, and SVR with the RBF kernel function provides the best performance. Plus, it can be effectively applied to model and control a practical biped walking robot.