A dual neural network for kinematic control of redundant robot manipulators

Author

Xia, Youshen ; Wang, Jun

Author_Institution

Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

31

Issue

1

fYear

2001

fDate

2/1/2001 12:00:00 AM

Firstpage

147

Lastpage

154

Abstract

The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is proven to be globally exponentially stable. The proposed dual network is also shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators

Keywords

motion control; neurocontrollers; recurrent neural nets; redundant manipulators; asymptotic tracking; dual network; dual neural network; inverse kinematics problem; kinematic control; motion control; recurrent neural network; redundant robot manipulators; time-varying quadratic optimization; Closed-form solution; Jacobian matrices; Kinematics; Manipulator dynamics; Neural networks; Neurons; Recurrent neural networks; Robot control; Robot sensing systems; Tracking;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.907574

Filename

907574