A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits

In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorpor...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 14; no. 3; pp. 658 - 667
Main Authors Zhang, Yunong, Wang, Jun, Xia, Youshen
Format Journal Article
LanguageEnglish
Published United States IEEE 01.05.2003
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ISSN1045-9227
DOI10.1109/TNN.2003.810607

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Summary:In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorporated into the problem formulation of redundancy resolution. Compared to other recurrent neural networks, the dual neural network is piecewise linear and has much simpler architecture with only one layer of neurons. The dual neural network is shown to be globally (exponentially) convergent to optimal solutions. The dual neural network is simulated to control the PA10 robot manipulator with effectiveness demonstrated.
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ISSN:1045-9227
DOI:10.1109/TNN.2003.810607