Support vector regression based inverse kinematic modeling for a 7-DOF redundant robot arm

Emre Sariyildiz, Kemal Ucak, Gulay Oke, Hakan Temeltas, Kouhei Ohnishi

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

In this paper, inverse differential kinematic modeling is performed for a 7-DOF (Degrees of Freedom) redundant robot arm. Two intelligent identification methods, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used for modeling. The main strengths of SVR over ANN are that it doesn't get stuck at local minima and it has powerful generalization abilities with very few training data. An important problem in inverse kinematic solutions are the singularities which are points in the operational space where manipulator Jacobian is not invertible. In this paper, simulations are performed on a PA-10 model, to compare the modeling performances attained by ANN and SVR. It has been observed that SVR outperforms ANN in inverse differential kinematic modeling. Training data is obtained using direct differential kinematic equations of the manipulator and data points close to singularities have been discarded.

本文言語English
ホスト出版物のタイトルINISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications
DOI
出版ステータスPublished - 2012
イベントInternational Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012 - Trabzon, Turkey
継続期間: 2012 7 22012 7 4

出版物シリーズ

名前INISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications

Other

OtherInternational Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012
国/地域Turkey
CityTrabzon
Period12/7/212/7/4

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信

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