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Target Tracking Algorithm for Table Tennis Using Machine Vision.

Hongtu Zhao1, Fu Hao2

  • 1Physical Education Institute, Jilin Normal University, Siping 136000, Jilin, China.

Journal of Healthcare Engineering
|June 7, 2021
PubMed
Summary

This study introduces an improved machine vision algorithm for table tennis robots, enhancing their ability to track ball trajectory and recognize ball spin. The enhanced robot system demonstrates improved adaptability and reaction time in gameplay.

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Table tennis robots struggle with high ball speeds and accurately identifying ball rotation (e.g., topspin, no spin).
  • Current systems exhibit limited adaptability due to a single return strategy, unable to account for varied ball movements.

Purpose of the Study:

  • To develop a novel target trajectory tracking algorithm for table tennis robots.
  • To enhance robot reaction time and adaptability by recognizing ball movement types and rotation.

Main Methods:

  • Machine vision combined with an improved Scaled Conjugate Gradient (SCG) algorithm.
  • Feature extraction from position and speed information of continuous video frames.
  • Deep neural network model trained on extracted features and historical data.

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Main Results:

  • The improved SCG algorithm addresses limitations of the original by incorporating an accuracy threshold and offline learning.
  • The developed algorithm effectively tracks table tennis ball trajectories and identifies ball rotation.
  • Experimental validation confirms the feasibility and applicability of the enhanced algorithm.

Conclusions:

  • The proposed machine vision and improved SCG algorithm significantly enhance table tennis robot performance.
  • The system demonstrates superior adaptability and suitability for real-time human-robot interaction in table tennis.