A dual neural network for constrained joint torque optimization of kinematically redundant manipulators

Authors:
Yunong Zhang;Jun Wang
Affiliations:
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, Shatin;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2002

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Abstract

A dual neural network is presented for the real-time joint torque optimization of kinematically redundant manipulators, which corresponds to global kinetic energy minimization of robot mechanisms. Compared to other computational strategies on inverse kinematics, the dual network is developed at the acceleration level to resolve redundancy of limited-joint-range manipulators. The dual network has a simple architecture with only one layer of neurons and is proved to be globally exponentially convergent to optimal solutions. The dual neural network is simulated with the PUMA 560 robot arm to demonstrate effectiveness.