Recurrent Neural Network for State Adjustment of Redundant Manipulators

Abstract

To make joints of a redundant manipulator moving automatically to a target state, a stateadjustment (SA) scheme is studied and modified in this paper. Specifically, owing to the problem of non-zero initial joint velocity in the SA scheme leading to a potential hazard to redundant manipulators, a modified state-adjustment (MSA) scheme is obtained on the basis of the SA scheme. The MSA scheme achieves the state adjustment by minimizing the differences between the joint angles and the target values. For solving the MSA scheme, a recurrent neural network (RNN) model is derived, of which the critical component is to iterate over the joint angles and joint velocities. The MSA scheme solved by the RNN model enables the redundant manipulator to adjust to the target state automatically while ensuring that the initial joint velocity is zero. Beyond that, several comparative simulations demonstrate the availability and accuracy of the MSA scheme solved by the RNN model in controlling the state adjustment of redundant manipulators. INDEX TERMS State-adjustment scheme, recurrent neural network, redundant manipulators.

Refined Self-Motion Scheme With Zero Initial Velocities and Time-Varying Physical Limits via Zhang Neurodynamics Equivalency

“…Similarly, the end-effector task may not be completed because of operating space limitations or manipulator physical limitations. Adjusting the manipulator configuration from one state to another state is essential and important for redundant robot manipulators (Jin et al, 2020 ). Thereinto, the self-motion of redundant robot manipulators is to adjust the manipulator configuration from the initial state to final state keeping the end effector immobile at its current position or orientation (Li and Zhang, 2012 ; Zhang et al, 2021a ).…”