Title: Spreadsheet-based neural networks modelling and simulation for training and predicting inverse kinematics of robot arm
Authors: Khairul Annuar Abdullah; Zuriati Yusof; Riza Sulaiman
Addresses: Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia; Faculty of Computer Science and Information Technology, Universiti Selangor, Bestari Jaya, 45600, Malaysia ' Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia ' Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
Abstract: This paper is proposed to solve the inverse kinematic (IK) problem of two-degree-of-freedom planar robot arm using neural networks (NN). Several NN model designs of distinct hidden neurons based on the sum of square error function of joint angle are developed and trained with generalised reduced gradient algorithm. The paper is also intended to demonstrate the modelling process of feed-forward NN topology in spreadsheet environment. The spreadsheet functions as INDEX, SUMPRODUCT, EXP, and SUMSQ; the utilities as name manager, data validation, data table, ActiveX controls, answer report, and charts; and the add-in Solver are utilised to develop the models. With the input parameters of link lengths and end-effector position and orientation, two models with the structures 5-12-1 and 5-10-1 are discovered best-capable in predicting first and second joint angles respectively. This NN-based IK technique contributes significantly to the optimal motion control of robot arm for quality processing and assembly tasks.
Keywords: feed-forward neural networks; generalised reduced gradient algorithm; inverse kinematics; multiple linear regression; robot arm; spreadsheet modelling and simulation.
International Journal of Computer Aided Engineering and Technology, 2018 Vol.10 No.3, pp.218 - 243
Received: 16 Mar 2016
Accepted: 03 Aug 2016
Published online: 12 Mar 2018 *