ARTICLE
Predicting the trajectory of a spinning ping pong ball during flight using three-dimensional coordinates
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Nanjing Forestry University, Department of Physical Education, Nanjing, Jiangsu, China
Submission date: 2023-08-17
Final revision date: 2023-12-20
Acceptance date: 2024-01-03
Online publication date: 2024-07-28
Publication date: 2024-07-31
Corresponding author
Xinyue Li
Department of Physical Education, Nanjing Forestry University, China
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ABSTRACT
Predicting the trajectory of a spinning ping pong ball can improve the effectiveness of a
ping pong robot in daily training. In this study, the Vicon system was used to capture
three-dimensional coordinates of the spinning ping pong ball during flight. Then, a long
short-term memory (LSTM) neural network algorithm was improved by combining an adap-
tive particle swarm optimization (APSO) algorithm and the attention mechanism, and the
APSO-LSTM-attention method was obtained for predicting the trajectory of the spinning
ping pong ball. It was found through experiments that the APSO-LSTM-attention method
had average displacement errors of 6.01mm, 11.26mm, and 8.97mm in the X, Y and Z
axes, respectively, and the final point displacement errors were 15.64mm, 17.93mm, and
11.26mm, respectively, indicating that the method outperformed methods such as recurrent
neural networks. The time required to predict the complete trajectory by the APSO-LSTM-
-attention method was also short, only 0.0186 s. The results demonstrate reliability of the
proposed method in predicting the trajectory of the spinning ping pong ball and its potential
application in practical scenarios.
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