A Posture of 6-DOF Manipulator By Locally Recurrent Neural Networks (LRNNs) Implement in Virtual Reality

Abstract

The paper presents a cognitive architecture for a posture of 6-DOF elbow manipulator with spherical wrist by using Locally Recurrent Neural Networks (LRNNs) and implements its movements in Virtual Reality (VR) environment. This design aims to allow the manipulator system to perform complex movement operations by solving the Inverse Kinematics Problem (IKP) with LRNNs using the position and orientation of end-effector which is represented by the wrist with 3- DOF. The Levenberg-Marquardt back propagation (LMBP) is used in the training of LRNNs which offered the high computation and accuracy for solving IKP for manipulator. The manipulator is built by using VR environment. This model permits direct forward dynamics simulation which accurately predicts wrist position and also present a solution to the inverse problem of determining a set of joints angle to achieve a given command for them posture of the manipulator. The simulation of the design is achieved by connecting the VR technique with Simulink\MATLAB Ver. 2009b to calculate the forward and inverse kinematics and implement the movements manipulator. Satisfactory results are obtained explaining the ability of implement the posture of 6-DOF manipulator by calculating the kinematics with LRNNs and implementing high complex movements.