Adaptive classification and strategy making system for android soccer games

This paper proposes an adaptive classification and strategy decision-making scheme which aims to integrate the image object identification and classification, the path planning, obstacle avoidance, and intelligent decision making technologies for android soccer games. It classifies the color block labels of the robots and the ball by using a statistical algorithm of kernel discriminant analysis firstly, and then determines the locations of the blocks based on image morphology operations. These color identification results will be offered the android soccer system to determine the detail object data of the robots and the ball for soccer game controls. Based on the training items of three kinds of AndroSot Challenges in 2011 FIRA World Cup, the proposed system intends to make role assignments of robots as attacker, defender, or goalkeeper, and to make decision of robot movements by formation strategies. Finally, the proposed scheme is implemented by actual competitions in 2011 FIRA AndroSot Game. The experimental results show the feasibility and the adaptability of the proposed system.