Design & Fabrication of TT Machine


EOI: 10.11242/viva-tech.01.05.0MECH_10

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Citation

Henisha Raut, Nikhil Santosh Sakpal, Sandesh Vikas Patil, Shrey Sunil Pawar, "Design & Fabrication of TT Machine", VIVA-IJRI Volume 1, Issue 7, Article MECH_10, pp. 1-6, 2024. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

This research introduces an innovative table tennis machine designed to revolutionize training for players of all levels. The compact device features an advanced robotic arm with versatile shot replication, from topspin to backspin. User-friendly customization, accessible through a mobile app or touchscreen, tailors settings to skill levels. Despite lacking an AI system, the sophisticated control system ensures precision in shot placement, speed, and frequency. A notable feature is the efficient ball-recycling system, reducing the need for manual reloading and enhancing practice efficiency. Rigorous testing, in collaboration with professional players and coaches, guarantees the machine's reliability. With its dynamic training features, this mechanical table tennis machine is set to transform the sport, providing a valuable tool for players and coaches alike, and contributing to the evolution of table tennis training standards.

Keywords

Ball-recycling system, Collaboration, Innovative, Robotic arm, User-friendly.

References

  1. Tesheng Hsiao, Hsuan-Che Kao, "Decision Making of Ball-Batting Robots Based on Deep Reinforcement Learning", 2023 American Control Conference (ACC), pp.782-787, 2023.
  2. Liu Yan, Sun Xin, "Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis", Computational Intelligence and Neuroscience, vol. 2021, Article ID 2436120, 11 pages, 2021.
  3. Jing Sun, Haochen Luo, and Huiqun Zhao “International Journal of Machine Learning and Computing”, Vol. 10, No. 1, January 2020.
  4. L. Draschkowitz, C. Draschkowitz, and H. Hlavacs, “Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis”, EAI Endorsed Trans Creat Tech, vol. 2, no. 5, Oct. 2015.
  5. M. Matsushima, T. Hashimoto, M. Takeuchi and F. Miyazaki, "A learning approach to robotic table tennis", IEEE Trans. Robot., vol. 21, no. 4, pp. 767-771, Aug. 2005.
  6. K. P. Modi, F. Sahin and E. Saber, "An application of human robot interaction: development of a ping-pong playing robotic arm", 2005 IEEE International Conference on Systems Man and Cybernetics, vol. 2, pp. 1831-1836, 2005.
  7. L. Acosta, J. J. Rodrigo, J. A. Mendez, G. N. Marichal and M. Sigut, "Ping-pong player prototype", IEEE Robotics & Automation Magazine, vol. 10, no. 4, pp. 44-52, Dec. 2003.
  8. F. Miyazaki, M. Takeuchi, M. Matsushima, T. Kusano and T. Hashimoto, "Realization of table tennis task based on virtual targets", Proc. IEEE Int. Conf. Robotics and Automation (ICRA), pp. 3844-3849, 2002.
  9. M. Bühler, D. E. Koditschek and P. J. Kindl-mann, "Planning and control of a juggling robot", International Journal of Robotics Research, vol. 13, no. 2, pp. 101-118, 1994.
  10. R. L. Andersson, "Aggressive trajectory generator for a robot ping pong player", IEEE Control System.
  11. M. Mokayef, M. K. A. A. Khan, M. H. D. A. Summakieh, L. Q. Jian, and M. Mokayef, “Embedded BallLauncher with Trajectory Path Analysis for Empowering Junior Table Tennis Players,” Journal ofAdvances in Artificial Life Robotics, vol. 4, no. 1, pp. 27–34, 2023, doi: 10.57417/jaalr.4.1_27.
  12. A. Dittrich et al., “AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA),IEEE, May 2023, pp. 3058–3064. doi: 10.1109/ICRA48891.2023.10160336.
  13. H.-I. Lin, Z. Yu, and Y.-C. Huang, “Ball Tracking and Trajectory Prediction for Table-Tennis Robots,”.Sensors, vol. 20, no. 2, p. 333, Jan. 2020, doi: 10.3390/s20020333.
  14. O. Koç, G. Maeda, and J. Peters, “Online optimal trajectory generation for robot table tennis,” Rob Auton Syst, vol. 105, pp. 121–137, Jul. 2018, doi: 10.1016/j.robot.2018.03.012.
  15. Z. Zhang, D. Xu, and M. Tan, “Visual Measurement and Prediction of Ball Trajectory for Table Tennis Robot,” IEEE Trans Instrum Meas, vol. 59, no. 12, pp. 3195–3205, Dec. 2010, doi:10.1109/TIM.2010.2047128.
  16. K. Dokic, T. Mesic, and M. Martinovic, “Table Tennis Forehand and Backhand Stroke Recognition Based on Neural Network,” 2020, pp. 24–35. doi: 10.1007/978-981-15-6634-9_3.