Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network

نویسندگان

چکیده

In this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving forward inverse kinematics of 3-DOF articulated robot. When designing MLFFNN kinematics, joints' variables are used as inputs to network, positions orientations robot end-effector outputs. case designed using only inputs, whereas joints’ For both cases, training proposed accomplished by Levenberg Marquardt (LM) method. A sinusoidal type motion variable frequencies commanded three joints manipulator, then data collected training, testing, validation processes. The experimental simulation results demonstrate that artificial inspired biological processes trained very effectively, indicated calculated mean squared error (MSE), which approximately equal zero. resulted in smallest MSE 4.592×10^(-8) 9.071×10^(-7). This proves highly reliable robust minimizing error. method applied a manipulator could be more complex types robots like 6-DOF or 7-DOF robots.

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ژورنال

عنوان ژورنال: International Journal of Robotics and Control Systems

سال: 2023

ISSN: ['2775-2658']

DOI: https://doi.org/10.31763/ijrcs.v3i2.1017