Welcome to Francis Academic Press

Frontiers in Sport Research, 2024, 6(3); doi: 10.25236/FSR.2024.060310.

Review on Robotic Application in the Field of Sports


Zirui Miao1, Gengyu Ge2

Corresponding Author:
Gengyu Ge

1School of Physical Education, Zunyi Normal University, Zunyi, China

2School of Information Engineering, Zunyi Normal University, Zunyi, China


With the updating of computer processing capabilities and the deep learning network models, artificial intelligence technology based on computer software and hardware has developed rapidly. As a physical carrier of artificial intelligence technology, robots, as a comprehensive technology that integrates multiple disciplines, have also rapidly developed and applied in multiple fields. Meanwhile, sports science studies are no longer limited to the study of sports and training, gradually introducing engineering techniques. This improves the traditional sports moving towards smart sports and increases the fun of physical education. By consulting literature and materials, this paper reviews the application of robots in the field of sports and lists some common robot cases. In addition, the robot technology of different sports projects is analysed and the relative positive significance for each sports project is explained. Finally, a summary and suggestions for the future trends were provided.


Interdiscipline; Information technology; Intelligence science; Robotic application; Sport

Cite This Paper

Zirui Miao, Gengyu Ge. Review on Robotic Application in the Field of Sports. Frontiers in Sport Research (2024) Vol. 6, Issue 3: 80-85. https://doi.org/10.25236/FSR.2024.060310.


[1] A. Liu, R.P. Mahapatra, A.V.R. Mayuri. Hybrid design for sports data visualization using AI and big data analytics [J]. Complex & Intelligent Systems, 2023, 9(3): 2969-2980.

[2] M.S. Yasar, T. Iqbal. A scalable approach to predict multi-agent motion for human-robot collaboration [J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1686-1693.

[3] R. Whall, C.A. Palmer. Developing an intelligent body–what does it mean to be physically educated? [J]. Journal of Qualitative Research in Sports Studies, 2021, 15(1): 77-104.

[4] L. Xu. Application analysis of sports robots based on pose recognition and action feature analysis [J]. International Journal of System Assurance Engineering and Management, 2023, 14(2): 519-528.

[5] J. Tebbe, Y. Gao, M. Sastre-Rienietz, et al. A table tennis robot system using an industrial kuka robot arm[C]//Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings 40. Springer International Publishing, 2019: 33-45.

[6] D. Büchler, S. Guist, R. Calandra, et al. Learning to play table tennis from scratch using muscular robots [J]. IEEE Transactions on Robotics, 2022, 38(6): 3850-3860.

[7] Gossard T, Tebbe J, Ziegler A, et al. SpinDOE: A ball spin estimation method for table tennis robot[C]//2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023: 5744-5750.

[8] Y. Wang, Z. Sun, Y. Luo, et al. A Novel Trajectory-Based Ball Spin Estimation Method for Table Tennis Robot[J]. IEEE Transactions on Industrial Electronics, 2023:1-11.

[9] Y. Sun, Y. Wang, C. Qu, et al. A novel ball-racket rebound model for table tennis robot based on continuous contact force[J]. IEEE Transactions on Instrumentation and Measurement, 2024.

[10] S. Mori, K. Tanaka, S. Nishikawa, et al. High-speed humanoid robot arm for badminton using pneumatic-electric hybrid actuators[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3601-3608.

[11] Z. Cao, T. Liao, W. Song, et al. Detecting the shuttlecock for a badminton robot: A YOLO based approach [J]. Expert Systems with Applications, 2021, 164: 113833.

[12] J. Xie, G. Chen, S. Liu. Intelligent badminton training robot in athlete injury prevention under machine learning[J]. Frontiers in neurorobotics, 2021, 15: 621196.

[13] H. Ye. Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things[J]. International Journal of Humanoid Robotics, 2023, 20(06): 2250018. 

[14] Narayanan A, Desai F, Stewart T, et al. Application of raw accelerometer data and machine-learning techniques to characterize human movement behavior: a systematic scoping review[J]. Journal of Physical Activity and Health, 2020, 17(3): 360-383.

[15] T. Xu, L. Tang. Adoption of machine learning algorithm-based intelligent basketball training robot in athlete injury prevention[J]. Frontiers in Neurorobotics, 2021, 14: 620378.

[16] J. Zhang, D. Tao. Research on deep reinforcement learning basketball robot shooting skills improvement based on end-to-end architecture and multi-modal perception[J]. Frontiers in Neurorobotics, 2023, 17: 1274543.

[17] S. Zhang, G. Zhao, P. Lin, et al. Deep Reinforcement Learning for a Humanoid Robot Basketball Player[C]//2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2023: 1-6.

[18] A.F. Rochim, D. Eridani, P.J. Rustam. Basketball Arm Shooting Robot Design by Implementing Parabolic Motion[C]//2023 6th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2023: 1-4. 

[19] F. Shi, X. Hu. Fuzzy dynamic obstacle avoidance algorithm for basketball robot based on multi-sensor data fusion technology[J]. International Journal of Foundations of Computer Science, 2022, 33(06n07): 649-666.

[20] J. Tian, H. Liu, S.L. Dai, et al. A real-time football goalkeeper robot system based on fuzzy logic control[C]//2021 China Automation Congress (CAC). IEEE, 2021: 3258-3263.

[21] Adarsh P, Rathi P, Kumar M. YOLO v3-Tiny: Object Detection and Recognition using one stage improved model[C]//2020 6th international conference on advanced computing and communication systems (ICACCS). IEEE, 2020: 687-694.

[22] S. Liu, G. Lever, Z. Wang, et al. From motor control to team play in simulated humanoid football [J]. Science Robotics, 2022, 7(69): eabo0235.

[23] D. Zhou, G. Chen, F. Xu. Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots[J]. Frontiers in Neurorobotics, 2022, 16: 867028.

[24] T. Haarnoja, B. Moran, G. Lever, et al. Learning agile soccer skills for a bipedal robot with deep reinforcement learning[J]. Science Robotics, 2024, 9(89): eadi8022.

[25] M. Wang, Z. Liang. Cross-modal self-attention mechanism for controlling robot volleyball motion [J]. Frontiers in Neurorobotics, 2023, 17: 1288463.

[26] M. Latifinavid, A. Azizi. Development of a vision-based unmanned ground vehicle for mapping and tennis ball collection: A fuzzy logic approach[J]. Future Internet, 2023, 15(2): 84.

[27] F. Gao, S. Li, Y. Gao, et al. Robots at the Beijing 2022 winter olympics[J]. Science Robotics, 2022, 7(65): eabq0785.

[28] C. Hong, I. Jeong, L.F. Vecchietti, et al. AI world cup: robot-soccer-based competitions[J]. IEEE Transactions on Games, 2021, 13(4): 330-341.

[29] D. Kim, S. Kim. A model for user acceptance of robot journalism: Influence of positive disconfirmation and uncertainty avoidance[J]. Technological Forecasting and Social Change, 2021, 163: 120448.

[30] T.C. Tsai, M.H. Chiang. A lower limb rehabilitation assistance training robot system driven by an innovative pneumatic artificial muscle system[J]. Soft Robotics, 2023, 10(1): 1-16.

[31] A. Harfoush, M. Hossam. Modelling of a robot-arm for training in fencing sport[J]. International Journal of Intelligent Robotics and Applications, 2020, 4(1): 109-121.

[32] C. Park, B. Kim, Y. Kim, et al. Carved turn control with gate vision recognition of a humanoid robot for giant slalom skiing on ski slopes[J]. Sensors, 2022, 22(3): 816.