Ball tracking and trajectory prediction system for tennis robots
Yoseph Yang, David Kim, Dongil Choi
Abstract
Abstract Recently, as the service robot market has grown, robots have emerged in various fields such as industry, service, and sports. In the field of sports, robots that can play with humans have been developed. We proposed a novel vision system for measuring the trajectory of a tennis ball and predicting its bound position, which can be utilized in the development of tennis robots. In this paper, we introduce a ball detection algorithm using an artificial neural network and a ball trajectory prediction algorithm using stereo vision. Our approach involved the use of a net vision system and a robot vision system to accurately detect and track the ball as it moves across the court. By combining these two systems, we were able to predict the trajectory and bound position of the tennis ball with high accuracy. As a result, the accuracy of the neural network for ball detection in actual tennis images reaches 81.4%. The ball trajectory prediction error in Gazebo simulation is 29.6 cm in the x-axis, 7.2 cm in the y-axis, and 11.7 cm in the z-axis on average.