A recurrent neural network for manipulator inverse kinematics computation

Author

Wu, Guang ; Wang, Jun

Author_Institution

Dept. of Ind. Technol., North Dakota Univ., Grand Forks, ND, USA

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2715

Abstract

A recurrent neural network is presented for the computation of inverse kinematics for redundant robot manipulators. The proposed recurrent neural network is based on a reflexive generalized inverse problem that simplifies the computation of pseudoinverses by reducing the number of matrix equations needed to be solved and the complexity of the physical implementation. The proposed recurrent neural network is shown to be asymptotically stable and is used to solve the inverse kinematics problem for a three degree-of-freedom planar redundant manipulator

Keywords

asymptotic stability; inverse problems; manipulator kinematics; recurrent neural nets; asymptotic stabilty; manipulator inverse kinematics; matrix equations; pseudoinverses; recurrent neural network; redundant robot manipulators; reflexive generalized inverse problem; three degree-of-freedom planar redundant manipulator; Closed-form solution; Computer networks; Inverse problems; Jacobian matrices; Kinematics; Manipulators; Neural networks; Nonlinear equations; Recurrent neural networks; Robots;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374660

Filename

374660