Application of minimum error entropy unscented Kalman filter in table tennis trajectory prediction

Table tennis is important and challenging project for robotics research, and table tennis robotics receives a lot of attention from academics. Trajectory tracking and prediction of table tennis is an important technology for table tennis robots, and its estimation accuracy is also disturbed by non-G...

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Published inPloS one Vol. 17; no. 9; p. e0269257
Main Authors Shenyue Luo, Jianfeng Niu, Peifeng Zheng, Zhihui Jing
Format Journal Article
LanguageEnglish
Published Public Library of Science (PLoS) 01.01.2022
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Summary:Table tennis is important and challenging project for robotics research, and table tennis robotics receives a lot of attention from academics. Trajectory tracking and prediction of table tennis is an important technology for table tennis robots, and its estimation accuracy is also disturbed by non-Gaussian noise. In this paper, a novel Kalman filter, called minimum error entropy unscented Kalman filter (MEEUKF), is employed to estimate the motion trajectory of physical model of a table tennis. 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.
ISSN:1932-6203
DOI:10.1371/journal.pone.0269257