Recurrent Neural Networks for Computing Pseudoinverses of Rank-Deficient Matrices
SIAM Journal on Scientific Computing
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A dual neural network for kinematic control of redundant robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
A Lagrangian network for kinematic control of redundant robot manipulators
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Improved neural solution for the Lyapunov matrix equation based on gradient search
Information Processing Letters
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In this paper, to diminish discontinuity points arising in the infinity-norm velocity minimization scheme, a bi-criteria velocity minimization scheme is presented based on a new neural network solver, i.e., an LVI-based primal-dual neural network. Such a kinematic planning scheme of redundant manipulators can incorporate joint physical limits, such as, joint limits and joint velocity limits simultaneously. Moreover, the presented kinematic planning scheme can be reformulated as a quadratic programming (QP) problem. As a real-time QP solver, the LVI-based primal-dual neural network is developed with a simple piecewise linear structure and high computational efficiency. Computer simulations performed based on a PUMA560 manipulator model are presented to illustrate the validity and advantages of such a bi-criteria velocity minimization neural planning scheme for redundant robot arms.