Inverse Kinematics Problem (IKP) of 6-DOF Manipulator by Locally Recurrent Neural Networks (LRNNs)

The paper presents a cognitive architecture for solution of inverse kinematics problem (IKP) of 6-DOF elbow manipulator with spherical wrist by Locally Recurrent Neural Networks (LRNNs) and simulated the solution by using MATLAB/Simulink. This design is aimed to allow the manipulator system to perform complex movement operations by solving the Inverse Kinematic Problem (IKP) with LRNNs by using the position and orientation of end-effector which represent by wrist with 3-DOF. The Levenberg-Marquardt back propagation (LMBP) is used in the learning of LRNNs which offered the high computation and accuracy for solving IKP for manipulator. This model permits direct forward dynamics simulation, which accurately predicts wrist position, also present a solution to the inverse problem of determining set of joints angle to achieve a given command for posture of manipulator. The simulation of design achieved by connect the program with SimulinkMATLAB Ver. 2009b to calculate the forward and inverse kinematic and implement the movements manipulator. Satisfactory results are obtained, that explains the ability of implement the posture of 6-DOF manipulator by calculate the kinematic with LRNNs and implement high complex movements.