Application of Kalman filter in track prediction of shuttlecock

This paper deals with the application of Kalman filter for optimizing and filtering the position signal of shuttlecock obtained by the vision servo system of ´shuttlecock robot´. Non-uniform mass distribution and air resistance effect can make much noise not only in vision recognition but also in kinematic model analysis of shuttlecock. The Kalman filter algorithm is used to filter the shuttlecock position signal by taking the error of measurement and the error of shuttlecock motion model into account. Besides, by considering the requirement of fast moving control, we reduce dimensions of state vector by decomposition of shuttlecock motion to shorten the executive cycle. The simulation results show its affectivity on improving the accuracy of track prediction. It can also accomplish track prediction fast and accurately when applied on `Shuttlecock Robot´.