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A novel method of shuttlecock trajectory tracking and prediction for a badminton robot

Published online by Cambridge University Press:  10 March 2022

Junming Zhi Open the ORCID record for Junming Zhi [Opens in a new window] ,
Deyuan Luo ,
Kui Li ,
Ya Liu  and
Huaqiu Liu
Junming Zhi*
Affiliation:
Mechanical Engineering, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, SiChuan Province, China
Deyuan Luo
Affiliation:
Mechanical Engineering, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, SiChuan Province, China
Kui Li
Affiliation:
Mechanical Engineering, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, SiChuan Province, China
Ya Liu
Affiliation:
Mechanical Engineering, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, SiChuan Province, China
Huaqiu Liu
Affiliation:
Mechanical Engineering, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, SiChuan Province, China
*
*Corresponding author. E-mail: junming818@qq.com
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Abstract

This paper proposes an aerodynamic analysis of the shuttlecock and a novel method for predicting shuttlecock trajectory. First, we have established a shuttlecock track data set by an infrared-based binocular vision system. Then the unscented Kalman filter algorithm is designed to further filter the noise and visual recognition algorithm errors. Third, the radial basis function (RBF)-based track prediction model is designed. This method offers a concept to obtain the neural network model of different kinds of flying or moving objects. The experimental results show that the proposed method can predict the shuttlecock trajectory in real time at high accuracy and can be used for implementing the algorithm of return strategies in the near future.

Keywords

Target trackingPredictionRBF network

Information

Type
Research Article
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1682 - 1694
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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