Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods

Abstract

In this paper, we propose a method for predicting the path of the ball on the soccer field for the humanoid robots. A cost-function-based k-nearest neighbor regression method is first proposed to account for the part of the prediction which is based on previously observed data. Next, the autoregression method is utilized in order to carry out the prediction based on the current ball path. Finally, these two methods are combined to form the final prediction model. Moreover, two different schemes are introduced based upon the proposed model: fixed and adaptive schemes. In fixed scheme, the prediction is made once during the initial steps of the motion and is used throughout the whole ball movement. However, in adaptive scheme, autoregression method coefficients are updated in fixed predefined steps during the motion. This is beneficial to robustify the prediction against an externally applied disturbance on the ball path. Our proposed method is tested by simulation and practical implementation and the results demonstrate a high precision rate.