Optimal Model Parameters of Inverse Kinematics Solution of a 3R Robotic Manipulator Using ANN Models

Nikolaos E. Karkalos, Angelos P. Markopoulos and Michael F. Dossis
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Nikolaos E. Karkalos: School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Angelos P. Markopoulos: School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
Michael F. Dossis: Department of Informatics Engineering, TEI of Western Macedonia, Kastoria, Greece

International Journal of Manufacturing, Materials, and Mechanical Engineering (IJMMME), 2017, vol. 7, issue 3, 20-40

Abstract: Solution of inverse kinematics equations of robotic manipulators constitutes usually a demanding problem, which is also required to be resolved in a time-efficient way to be appropriate for actual industrial applications. During the last few decades, soft computing models such as Artificial Neural Networks (ANN) models were employed for the inverse kinematics problem and are considered nowadays as a viable alternative method to other analytical and numerical methods. In the current paper, the solution of inverse kinematics equations of a planar 3R robotic manipulator using ANN models is presented, an investigation concerning optimum values of ANN model parameters, namely input data sample size, network architecture and training algorithm is conducted and conclusions concerning models performance in these cases are drawn.

Date: 2017
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