Fuzzy decision machine (FDM) compared with finite state machine is proposed in this article to verify the appropriate performance on the soccer robot platform. Fuzzy logic with the flexible membership function adaptively produces the robust strategy for real-time reaching the better decision action for soccer robots working in the higher competition environment. This study feeds actual physical parameters into the fuzzy soccer robot system by a Gazebo simulator to ensure the perfect visibility. Therefore, the deployed 3D Gazebo scene for the specific Federation of International Robot-soccer Association (FIRA) RoboSot competition fields is greatly supplied with soccer robots as a friendly support. Moreover, four strategies are inspirited from experts’ experiences to implement the strong fuzzy rules through Robot Operating System (ROS). Fuzzy decision system infers the attacked or defended mode with the respective robot state information to select the near optimal robot action. The primary team’s object is to quickly attack the opponent’s goal when the fighting condition is favorable for the team. In the other case, the tactic is changed to reduce the probability of intercepting by the backswing way. FDM compares with the finite state machine in soccer robot competition fields to verify its availability. This experiment shows that the addressed FDM transforms the expert’s knowhow into appropriate fuzzy rules to improve the winning rates. Therefore, the generated soccer robot system contains the ability to make the best reactions in real time for approaching the desired home goals. The ROS-based system fuzzy adaptive machines embody the faculties to approach the performed strategy in real platform for getting the higher wining rate in the dynamic, complex and uncertain soccer robot competition games.