A two-layer recurrent neural network for redundancy resolution of manipulators through joint torque minimization

Abstract

A two-layer recurrent neural network for redundancy resolution of manipulators with local joint torque minimization is presented. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the command signals of desired acceleration of the end-effector are fed into the input layer, the output layer generates the joint acceleration vector of the manipulator with joint torque being minimized. The redundancy resolution obtained by the proposed recurrent neural network approach is compared with that obtained by previous literature. The proposed recurrent neural network is shown to be capable of generating redundancy resolution of manipulators with global stable minimized joint torque in real time.