A Learning Model for Racket Motion Decision in Ping‐Pong Robotic System

Abstract

In this paper, a model of computing the desired velocity of returning balls is developed so that the robot could return the incoming ball to a desired point on the table with specified landing velocity. At first, three polynomials of the flying time are applied to fit the ball's trajectory while neglecting the effort of Magnus force; one result of returning velocity is obtained after the coefficients of the polynomials are estimated using the LM (Levenberg‐Marquardt) algorithm. Then a new configuration based on area segmentation is proposed which is used in storing and replacing experimental data; another result of the velocity is received by fitting the stored data. The weighted average of the two above mentioned results is regarded as the original value of the fuzzy correcting algorithm. With the analysis of the influence of the desired velocity on the errors between the actual landing point and the desired point, the fuzzy rule is designed. The eventual returning velocity of the ball is obtained using the fuzzy correcting algorithm based on the errors of the landing point predicted by the flying model. Finally, experiments are conducted to verify the performance of the proposed method.

Visual Inspection and Batting Decision of Table Tennis Robot

“…According to the landing point error predicted by the flight model, he uses a fuzzy correction algorithm to obtain the final return speed of the ball. Finally, he verified the effectiveness of the proposed method through experiments [30].…”