Abstract
In this paper we present a kinematic based trajectory tracking application of redundant planar robot arm by using support vector machine method (SVM). The main advantages of using the proposed method are that, it does not suffer from singularity that is the main problem of redundancy in robot kinematics and better results for the kinematic model of redundant robot arm can be obtained by using less training data. Training data are obtained by using the forward differential kinematic model of the robot arm. We also implement the trajectory tracking application by using Artificial Neural Networks (ANN). Two methods are compared with respect to their generalization performances, and training performance. Simulation results are given.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, P.H.: A Closed-Form Solution for Inverse Kinematics of Robot Manipulators with Redundancy. IEEE Journal of Robotics and Automation RA-3(5) (October 1987)
Yoshikawa, T.: Analysis and Control of Robot Manipulators with Redundancy. In: Robotics Research–The First International Symposium, pp. 735–747. MIT Press, Cambridge (1984)
Uchiyama, M.: Study in Dynamic Control of Artificial Arm-Part I. Trans. JSME 45(391), 314–322 (1979)
Ping, G., Wei, B., Li, X., Luo, X.: Real-Time Obstacle Avoidance for Redundant Robot. In: IEEE International Conference on Mechatronics and Automation, pp. 223–228 (August 2009)
Lin, C.C.: Motion Planning of Redundant Robots. Journal of Robotic Systems 14(12), 839–850 (1997)
Wolovich, W.A., Elliott, H.: A Computational Technique for Inverse Kinematics. In: Proceedings of 23rd Conference on Decision and Control (December 1984)
Caccavale, F., Chiacchio, P., Chiaverini, S., Siciliano, B.: Experiments of Kinematic Control on a Redundant Robot Manipulator. Laboratory Robotics and Automation 8, 25–36 (1996)
Siciliano, B., Khatib, O.: Handbook of Robotics. Springer, Heidelberg (2008)
Hu, Z., Fu, Z., Fang, H.: Study of singularity robust inverse of Jacobian matrix for manipulator. In: International Conference on Machine Learning and Cybernetics, Beijing, pp. 406–410 (2002)
Nakamura, Y., Hanafusa, H.: Inverse kinematic solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement and Control 108, 163–171 (1986)
Wampler, C.W.: Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods. IEEE Transactions on Systems, Man and Cybernetics 16, 93–101 (1986)
Balestrino, A., De Maria, G., Sciavicco, L.: Robust control of robotic manipulators. In: International Proceedings of the 9th IFAC World Congress, Budapest, vol. 6, pp. 80–85 (1984)
Lin, S., Huang, C.W.: Numerical Integration of Multibody Mechanical Systems Using Baumgarte’s Constraint Stabilization Method. Journal of the Chinese Institute of Engineers 25(2), 243–252 (2002)
Hasan, A.T., Ismail, N., Hamouda, A.M.S., Aris, I., Marhaban, M.H., Al-Assadi, H.M.A.A.: Artificial Neural Network-Based Kinematics Jacobian Solution for Serial Manipulator Passing through Singular Configurations. In: Advances in Engineering Software 41, 359–367 (2010)
Karilk, B., Aydin, S.: An improved approach to the solution of inverse kinematics problems for robot manipulators. J. Eng. Appl. Artif. Int. 13, 159–164 (2000)
Ogawa, T., Matsuura, H., Kanada, H.: A Solution of inverse kinematics of robot arm using network inversion. In: Proceedings of the 2005 International Conference on Computational Intelligence for Modelling (2005)
Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot Modeling and Control. Wiley, Chichester (2005)
Craig, J.J.: Introduction to robotics: Mechanics and Control, 3rd edn. Prentice-Hall, Englewood Cliffs (2006)
Gunn, S.: Support Vector Machines for Classification and Regression, ISIS Technical Report (1998)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Statistics and Computing 14, 199–222 (2004)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Suykens, J.A.K.: Nonlinear Modeling and Support Vector Machines. In: IEEE Instrumentation and Measurement Technology Conference Budapest, Hungary, vol. 1, pp. 287–294 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sariyildiz, E., Ucak, K., Oke, G., Temeltas, H. (2011). A Trajectory Tracking Application of Redundant Planar Robot Arm via Support Vector Machines. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2011. Lecture Notes in Computer Science(), vol 6943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23857-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-23857-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23856-7
Online ISBN: 978-3-642-23857-4
eBook Packages: Computer ScienceComputer Science (R0)