Developing a vision module for a humanoid ping-pong game is challenging due to the spin and the non-linear rebound of the ping-pong ball. In this paper, we present a robust predictive vision module to overcome these problems. The hardware of the vision module is composed of two stereo camera pairs with each pair detecting the 3D positions of the ball on one half of the ping-pong table. The software of the vision module divides the trajectory of the ball into four parts and uses the perceived trajectory in the first part to predict the other parts. In particular, the software of the vision module uses an aerodynamic model to predict the trajectories of the ball in the air and uses a novel non-linear rebound model to predict the change of the ball's motion during rebound. The average prediction error of our vision module at the ball returning point is less than 50 mm -a value small enough for standard sized ping-pong rackets. Its average processing speed is 120fps. The precision and efficiency of our vision module enables two humanoid robots to play ping-pong continuously for more than 200 rounds.
In REF , the authors focus on the physical models useful to predict the ball trajectory, and use these models for humanoid robot table tennis.
A Robust Vision Module for Humanoid Robotic Ping-Pong Game
International Journal of Advanced Robotic Systems
Computer Science