Return Strategy and Machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition - Wang, Yuxuan; Yang, Xiaoming; Wang, Lili; Hong, Zheng; Zou, Wenjun (Frontiers in Neurorobotics, 2022)
At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and improve the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the start point to recognize and analyze the return action of the tennis sports robot. Openpose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the back swing, forward swing and volatilization are high in the return strategy of tennis sports robots. Preparation action, back swing and volatilization can achieve more objective results in the analysis of return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best.