![]() ![]() Where p, v, and w denote the position, linear velocity, and angular velocity of the table tennis, respectively. The state of the table tennis is represented by using the vector x k with the following form The simulation results show that the MEEUKF algorithm shows outstanding performance in tracking and predicting the trajectory of table tennis compared to some existing algorithms. In this study, a physical model that takes into account the spin of table tennis is used, and we use MEEUKF to estimate this non-linear model in the presence of non-Gaussian noise. ![]() These algorithms based on the MEE criterion are able to achieve outstanding performance in estimating the state of a non-linear non-Gaussian model. To cope with the problem of state estimation of nonlinear system, the minimum error entropy unscented Kalman filter (MEEUKF) and cubature information filter to be based on minimum error entropy (MEE-CIF) are derived. Moreover, the criterion is widely used in adaptive filter and Kalman filter. Minimum error entropy (MEE) criterion, in ITL, is a powerful tool in processing original data mixed with non-Gaussian noise. In recent years, information theoretic learning (ITL) is widely used in the context of state estimation problem for deterministic models. The physical model of table tennis is non-linear and the observations are often disturbed by impulse noise, which poses difficulties for the trajectory of the ball. Some vision system that can predict the trajectory of spinning table tennis has been developed. A physical model that takes into account the rotation of a table tennis is applied to a table tennis robot. In order to obtain more accurate tracking, the application of more accurate models in table tennis trajectory tracking studies is extremely necessary. A physical flight models that does not take into account the spin of a table tennis. A algorithm based on the forces applied approximate physical model for predicting the trajectory of a table tennis is proposed. The physical model of table tennis is very important for the trajectory and prediction of its trajectory. This paper focuses on the estimation and prediction of the trajectory of table tennis.Īccurate trajectory estimation is essential for hitting the table tennis and winning the game for robotic table tennis. Accurate estimation of the trajectory of a table tennis is fundamental to the sport for robots. It is also an important and challenging project for robotics research. Table tennis is a interesting game for humans to master, and it is also an indispensable part of human life. ![]()
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