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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Published in | IEEE transactions on neural networks Vol. 14; no. 3; pp. 658 - 667 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.05.2003
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Subjects | |
Online Access | Get full text |
ISSN | 1045-9227 |
DOI | 10.1109/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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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 1045-9227 |
DOI: | 10.1109/TNN.2003.810607 |