RBF Neural Network Based Kinematic Calibration of a Planar Parallel Robot

Author

Ding, Qingyong ; Li, Zhipeng

Author_Institution

Inst. of Robotics, Harbin Inst. of Technol., Heilongjiang

Volume

2

fYear

0

fDate

0-0 0

Firstpage

8059

Lastpage

8063

Abstract

This paper presents the kinematic calibration of a planar parallel robot. A radial based function (RBF) neural network based nonparametric method is proposed, in which the network is used to store and interpolate the joint correction. The experimental results show that it works more effectively than nonlinear regression based model parameter identification and spline interpolation based joint correction. This is because the method is free from validity of model and approximates the kinematic behavior of the actual robot more accurately. The accuracy is improved from 1.66 mm (maximum) and 0.99 (average) mm to 0.0284 (maximum) 0.0158 mm (average) by the proposed method

Keywords

calibration; interpolation; radial basis function networks; robot kinematics; splines (mathematics); RBF neural network based kinematic calibration; joint correction interpolation; planar parallel robot; radial based function; Calibration; Educational institutions; Interpolation; Kinematics; Neural networks; Parallel robots; Parameter estimation; Robot sensing systems; Spline; Telecommunication traffic; Kinematic calibration; RBF neural network; accuracy; planar parallel robot;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713543

Filename

1713543