Abstract:

With the increasing awareness of health and the popularity of competitive sports, the technological and professional development of ball sports training has become a significant trend. In football training, precise simulation of shooting trajectories and the design of personalized training programs are critical issues that need to be addressed. This study establishes an intelligent ballistic trajectory simulation football training robot system, integrating shooting mechanisms, visual acquisition, data analysis, and motion control technologies, aimed at enhancing the scientific and effective nature of training. The system features a three-axis gimbal shooting robot with omnidirectional movement capabilities, allowing it to flexibly adjust shooting angles and positions to meet various training needs. By utilizing an optimized RMSProp algorithm, the robot achieves the function of reverse solving launch parameters, enabling precise adjustments of yaw and pitch angles based on target positions. Experimental results indicate that the robot maintains a shooting point error of less than 0.45 meters under various training conditions, with a root mean square error of less than 7.5 centimeters between theoretical and actual trajectories, validating the system's robustness and accuracy. Additionally, we have established a detailed shooting dataset that provides important resources for future research in data science and artificial intelligence. This research promotes the intelligent development of football training, offering athletes a more scientific training tool and enhancing the overall level of football performance.

Key words: football robot, ballistics trajectory simulation, RMSProp algorithm