TY - GEN
T1 - Support vector regression based inverse kinematic modeling for a 7-DOF redundant robot arm
AU - Sariyildiz, Emre
AU - Ucak, Kemal
AU - Oke, Gulay
AU - Temeltas, Hakan
AU - Ohnishi, Kouhei
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Artificial Neural Networks
KW - Redundancy
KW - Robot Arm
KW - Singularity
KW - Support Vector Machine
KW - Trajectory Tracking
UR - http://www.scopus.com/inward/record.url?scp=84866605723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866605723&partnerID=8YFLogxK
U2 - 10.1109/INISTA.2012.6247033
DO - 10.1109/INISTA.2012.6247033
M3 - Conference contribution
AN - SCOPUS:84866605723
SN - 9781467314466
T3 - INISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications
BT - INISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications
T2 - International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012
Y2 - 2 July 2012 through 4 July 2012
ER -