Spin observation and trajectory prediction of a ping-pong ball

For ping-pong playing robots, observing a ball and predicting a ball´s trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball´s position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball´s spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball´s motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.