Title: A radial basis function network approach to approximate the inverse kinematics of a robotic system
Authors: Bach H. Dinh; Matthew W. Dunnigan; Zool H. Ismail
Addresses: Electrical Engineering Department, Ton Duc Thang University, Tan Phong Ward, District 7, Hochiminh City, Vietnam ' Department of Electrical, Electronic and Computer Engineering, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, UK ' Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Jalan Semarak, 54100, Kuala Lumpur, Malaysia
Abstract: This paper presents a novel solution using a radial basis function network (RBFN) to approximate the inverse kinematics of a robotic system where the geometric parameters of the manipulator are unknown. Simulation and experimental results are presented for a three-link manipulator to demonstrate the effectiveness of the proposed approach. To achieve this level of performance, centres of hidden-layer units are regularly distributed in the workspace, constrained training data is used where inputs are collected approximately around the centre positions in the workspace and the training phase is performed using either strict interpolation or the least mean square algorithm. These proposed ideas have significantly improved the network's performance.
Keywords: radial basis function; RBF; neural networks; RBFN; inverse kinematics; robotic manipulators; visual measurement; robot kinematics; simulation; interpolation; least mean squares.
International Journal of Modelling, Identification and Control, 2014 Vol.21 No.2, pp.113 - 124
Available online: 24 Mar 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article