Predicting a moving object position for visual servoing: theory and experiments

Abstract

Abstract In order to perform visual servoing tasks in a robotic system, one is confronted with the low sampling rate of standard cameras and the time delay introduced by image processing. One way to circumvent the time‐delay problem is to estimate future positions of the moving object of interest employing prediction techniques. In this work, three prediction techniques, namely Kalman filtering and two adaptive techniques employing least squares with forgetting factor and the projection algorithm, respectively, are evaluated in terms of their prediction error. Experimental results show that the adaptive techniques give best results and the Kalman filter‐based predictor shows a high sensitivity to velocity changes of the moving object. Copyright © 2001 John Wiley & Sons, Ltd.