Development of Badminton Tactical Simulation and Scenario Training System Based on Generative AI
Jiming Ma, Long Hao, Kun Qian, Jiashun Long, Li Li, Yanling Zhong · 2025
At present, badminton tactical training has problems such as single confrontation scenario and insufficient dynamic strategy generation. Traditional simulation methods rely too much on coaching experience and fixed playback videos, which is difficult to meet the needs of athletes’ personalized tactical awareness training and adaptive training in complex confrontation environments. In response to the above problems, this study proposes a tactical simulation and scenario training system based on generative AI. First, this paper constructs a multidimensional tactical knowledge graph and establishes a tactical ontology model including spatial trajectory, hitting mode, and confrontation strategy; secondly, a dynamic confrontation scenario generation module based on conditional generative adversarial network (CGAN) is developed. Finally, a tactical optimization engine driven by the Proximal Policy Optimization (PPO) algorithm is designed to dynamically adjust the difficulty coefficient and strategy complexity of the generated scenarios. In the experiment, the athletes in the experimental group using the system achieved an accuracy rate of 85.4% in tactical prediction, which is significantly higher than the 75.1% of the control group; in terms of multi-shot connection error rate, the experimental group is 12.0 %,while the control group is17.2 %. These results show that this system can provide a personalized and diversified training environment and significantly improve athletes’ tactical awareness and technical connection ability.