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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.

The purpose of this paper is to design and implement a team of middle size soccer robots to conform RoboCup middle‐size league.

First, according to the rules of RoboCup, a middle size soccer robot was designed. The proposed autonomous soccer robot consists of the mechanical platform, motion control module, omni‐directional vision module, front vision module, image processing and recognition module, investigated target object positioning and real coordinate reconstruction, robot path planning, competition strategies, and obstacle avoidance. This soccer robot equips the laptop computer system and interface circuits to make decisions.

In fact, the omni‐directional vision sensor of the vision system deals with the image processing and positioning for obstacle avoidance and target tracking. The boundary‐following algorithm is applied to find the important features of the field. The sensor data fusion method is utilized in the control system parameters, self‐localization, and world modeling. A vision‐based self‐localization, and the conventional odometry systems are fused for robust self‐localization. The localization algorithm includes filtering, sharing, and integration of the data for different types of objects recognized in the environment.

This paper presents results of research work in the field of autonomous robot‐middle size soccer robot supported by IAU‐Khorasgan Branch (Isfahan).

The purpose of this paper is to design and implement a team of kid size humanoid soccer robots conforming to RoboCup Humanoid league.

The project is described in two main parts: hardware and software. The hardware section consists of the mechanical structure and the driver circuit board enabling each robot to walk, fast walk, autonomously get up, kick and dribble when it catches the ball. The software is developed as a robot application which consists of motion controller, autonomous motion robot, self localization based on vision system, AI, trajectory planning and network.

This year, the authors' developments for the humanoid robot include: the design and construction of our new humanoid robots structure and implementation of a new recurrent hybrid neural network for walking control. The control system consists of two neural network controllers, two standard PD controllers and a robot walking planar. The proposed neural network controller has three layers, which are input, hidden, and output layers.

This paper presents results of research work in the field of autonomous robot‐middle size soccer robot, supported by IAU‐ Isfahan Branch (Khorasgan).