Title :
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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374660
