Robot geometric parameter identification with extended Kalman filtering algorithm

Abstract

This paper proposes a calibration method for enhancing position accuracy of robotic manipulators. In order to increase the robot accuracy, the method first develops a robot kinematic model and then identifies the robot geometric parameters by using an extended Kalman filtering (EFK) algorithm. The Kalman filter has advantages in identifying geometric parameters from the noisy measurements. Therefore, the obtained kinematic parameters are more precise. A simulation study of this calibration is performed for a PUMA 560 robot to prove the effectiveness of the method in increasing robot position accuracy. © Springer-Verlag Berlin Heidelberg 2013.