Development of local vision-based behaviors for a robotic soccer player

Abstract

This research focuses on the development of local vision-based behaviors for the robotic soccer domain. The behaviors, which include finding ball, approaching ball, finding goal, approaching goal, shooting and avoiding, have been designed and implemented using a robust layered control system. The avoiding behavior was learned using the C4.5 decision tree algorithm, the rest of the behaviors were programmed by hand. We describe the vision system employed by a mobile robot. Additionally, we compare two pixel classification techniques. One technique is based on the fast and cheap color image segmentation for interactive robots and the other one is based on the artificial life paradigm. We describe experimental results obtained using a Pioneer 2-DX robot equipped with a single camera, playing on an enclosed soccer field with forward role.