Abstract
A novel neural network (NN) based inverse kinematics solution of redundant manipulators is proposed to solve the joint limits problem. An adaptive learning algorithm for that NN is derived based on Lyapunov approach. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. Experiments are implemented for the PA-10 redundant manipulator and a comparative study is made with the gradient projection method.
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
M. Liegeois, “Automatic supervisory control of the configuration and behavior of multi-body mechanisms,” IEEE Trans. Syst., Man, Cybern., vol. SMC-7, no. 12, pp. 868–871, 1977.
H. Zghal, R. V. Dubey, and J. A. Euler, “Efficient gradient projection optimization for manipulators with multiple degrees of redundancy,” in IEEE Int. Conf. Robot. and Automat., vol. 2, 1990, pp. 1006–1011.
T. F. Chan and R. V. Dubey, “A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators,” IEEE Trans. Robot. and Automat., vol. 11, no. 2, 1995, pp. 286–292.
B. Nelson and P. K. Khosla, “Strategies for increasing the tracking region of an eye-in-hand system by singularity and joint limits avoidance,” Int. J. Robot. Res., vol. 14, no. 3, June, 1995, pp. 255–269.
F. Chaumette and E. Marchand, “A redundancy-based iterative approach for avoiding joint limits: application to visual servoing,” IEEE Trans. Robot. and Automat., vol. 17, no. 5, Oct., 2001, pp. 719–730.
Y. Zhang, J. Wang and Y. Xu, “A dual neural network for bi-criteria kinematic control of redundnat manipulators,” IEEE Trans. Robot. and Automat., vol. 18, no. 6, Dec., 2002, pp. 923–931.
Y. Zhang, J. Wang and Y. Xia, “A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits,” IEEE Trans. Neural Networks, vol. 14, no. 3, May, 2003, pp. 658–667.
J. S. R. Jang and C. T. Sun, “Neuro-fuzzy modeling and control,” Proc. of the IEEE, vol. 83, no. 3, Mar. 1995, pp. 378–406.
B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: perceptron, madaline, and backpropagation,” Proc. of the IEEE, vol. 78, no. 9, Sept. 1990, pp. 1415–1442.
S. Haykin, Neural Networks: A Comprehensive Foundation, New York: Macmillan, 1994.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Assal, S.F.M., Watanabe, K., Izumi, K. (2005). Cooperative Fuzzy Hint Acquisition for Industrial Redundant Robots to Avoid the Joint Limits. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_12
Download citation
DOI: https://doi.org/10.1007/3-540-32391-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
eBook Packages: EngineeringEngineering (R0)