In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision‐making system of a soccer robot system should have the features of flexibility and real‐time adaptation. The purpose of this paper is to focus on the middle‐size soccer robot league (MSL) and present new hierarchical hybrid fuzzy methods for decision making and action selection of an MSL robot.
In this paper, new hierarchical hybrid fuzzy methods for decision making and action selection of a robot in MSL are presented. First, the behaviors of an agent are introduced, implemented and classified in two layers, the low‐level behaviors and the high‐level behaviors. In the second layer, a two‐phase mechanism for decision making is introduced. In phase one, some useful methods are implemented which check the robot's situation for performing required behaviors. In the next phase, the team strategy, team formation, robot's role and the robot's positioning system are introduced. A fuzzy logical approach is employed to recognize the team strategy and furthermore to tell the player the best position to move.
This methodology was implemented on the ADRO RoboCup Team and ADRO team performance 2008 was compared with its previous version 2007. The results showed the success of this methodology; the team performance in coordination and collaboration highly improved; in fact, the players switched their strategic area smoothly as the team strategy changed in a reasonable manner, the robots carried out the high‐level behaviors much more efficiently and the final results were enhanced significantly.
This paper is a result of the authors' original research work in the field of autonomous robot‐middle size soccer robot, supported by Islamic Azad University – Khorasgan Branch, Isfahan, Iran.
