Recurrent neural network for manipulator inverse kinematics computation

Guang Wu, Jun Wang

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages2715-2720
Number of pages6
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period27/06/9429/06/94

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