Application and research of trajectory prediction of table tennis robot based on LSTM

Abstract

With the development of science and technology, table tennis robots are becoming more and more common in the daily competition and training of athletes. This study is mainly for the research and development of the vision of table tennis robots. This study uses long and short time memory network (LSTM) to predict table tennis trajectory. We collected a large amount of table tennis sport data and used it to train and test our LSTM model. By analyzing the motion trajectory of the ball, we designed an effective LSTM architecture that can capture the complex dynamic properties of table tennis movements. Experimental results show that our model achieves remarkable success in table tennis trajectory prediction, with higher accuracy and robustness compared to traditional methods.