Cheng Zhou, Chenhui Pei
School of Physical Education, Hunan University of Arts and Science, Changde 415000, Hunan, China
With the rapid progress and development of modern information technology, deep learning and other science and technology, as well as the wide application of Internet of things technology in China, intelligent robot has become the subject and focus of attention needed by various major research departments. The progress and development of intelligent robot technology will bring unprecedented threat and influence to the whole human and social development in the next few years, making more human survive and extricate themselves from the simple and heavy physical labor, and promoting the improvement and development of the whole society and productivity. This paper focuses on the application of intelligent robot technology in sports, and knows the development trend of sports in the future. In this paper, the application of intelligent robot in football is used to reflect the technology and problems needed by future sports. While learning the traditional robot environment perception technology and robot autonomous learning technology, such as robot ranging technology, target object perception and recognition technology, aiming at the design and implementation of robot system architecture, environment perception and modeling, object recognition, robot human action learning and other issues, this paper designs and implements a system that is helpful for robot decision-making and execution. In this paper, through the test of the system, it is found that the static ranging error of the ranging model within 4 meters is less than 3%. The average speed of the intelligent soccer robot forward 4 meters is 0.15 meters per second, the average speed of backward 3 meters is 0.1 meters per second, and the average speed of traverse 3 meters is 0.06 meters per second. All of these can meet the actual needs, which shows that the system has a good role in promoting the development of sports in the future.
Future Sports, Intelligent Robot Technology, Executive Decision System, Ranging Model
Cheng Zhou, Chenhui Pei. The Future Training of Sports Intelligent Robot Technology. International Journal of Frontiers in Sociology (2021), Vol. 3, Issue 13: 7-13. https://doi.org/10.25236/IJFS.2021.031302.
 Kerr C H, Bb B, Gh C. Future Directions in Sports-Related Concussion Management – Science Direct [J]. Clinics in Sports Medicine, 2021, 40(1):199-211.
 Wang T M, Tao Y, Liu H. Current Researches and Future Development Trend of Intelligent Robot: A Review [J]. International Journal of Automation and Computing, 2018, 15(05):15-36.
 Kim, J. H., et al. "[Advances in Intelligent Systems and Computing] Robot Intelligence Technology and Applications 5 Volume 751 || Intelligent Smart Glass for Visually Impaired Using Deep Learning Machine Vision Techniques and Robot Operating System (ROS)." 10.1007/978-3-319-78452-6.Chapter 10(2019):99-112.
 Zhou A L, Wang D C. Analysis of Intelligent Casting and Present Status and Key Technology of Robot Application [J]. Zhuzao/Foundry, 2018, 67(1):11-13.
 Riva C. TERESA: Socially Intelligent Robot as Window to the World [J]. Cyberpsychology Behavior & Social Networking, 2017, 20(5):343.
 Mohammed, Aqeel, A, et al. New Algorithm for Autonomous Dynamic Path Planning in Real-Time Intelligent Robot Car [J]. Journal of Computational and Theoretical Nanoscience, 2017, 14(11):5499-5507.
 Miao Z, Jiang Y, Zhang H, et al. Research on intelligent robot systems for emergency prevention and control of major pandemics [J]. Scientia Sinica Informationis, 2020, 50(7):1069.
 Qi Y, Ke Y. Fast Path Planning for On-Water Automatic Rescue Intelligent Robot Based on Constant Thrust Artificial Fluid Method[J]. Scientific Programming, 2020, 2020(1):1-13.
 Haiyang L I. Reliability Evaluation of Bearings in theIntelligent Robot for Changing the Hobwithout Failure Data [J]. Journal of Mechanical Engineering, 2019, 55(2):186.
 V, B, Melekhin. Model of Representation and Acquisition of New Knowledge by an Autonomous Intelligent Robot Based on the Logic of Conditionally Dependent Predicates [J]. Journal of Computer and Systems Sciences International, 2019, 58(5):747–765.
 Zhang, X., J. Zhang, and J. Zhong. "Skill Learning for Intelligent Robot by Perception-Action Integration: A View from Hierarchical Temporal Memory." Complexity 2017(2017):1-16.
 Yao, C., et al. "[IEEE 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Wuhan, China (2017.6.16-2017.6.18)] 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Research on the UAV multi-channel human-machine interaction system." (2017):190-195.