Detection of basic behaviors in logged data in robocup small size league

Abstract

This paper describes a method that extracts the basic behaviors of robots such as kicking and passing from the history data of the positions and velocities of the robots and the ball in RoboCup Small Size League (SSL). In this paper, as a first step, we propose an offline method that extracts the basic behaviors of robots from the logged data which is a record of the positions and velocities of the robots and the ball as the time series data. First, paying attention to the ball movement, we extract the line segments in the ball trajectory which satisfy our proposed conditions. These segments arise from the kicking actions. Then we classify the extracted line segments into the detailed kicking actions by analysing the intention of the kicked robot. We also propose algorithms that detect and classify the covering actions. Experimental results show that 98% of the kicking actions are correctly detected and more than 80% of the detected kicking actions are correctly classified, and that 90% of the covering actions are also correctly classified. © 2009 Springer Berlin Heidelberg.