In this paper we present a fuzzy_IAMR Intelligent Autonomous Mobile Robot navigation approach of Autonomous Mobile Robot. The robot has to find a collision-free trajectory between the starting configuration and the goal configuration in a static unknown environment containing some obstacles. To deal with autonomy requirements and to present a real intelligent task, the use of the Fuzzy Logic FL has an advantage of adaptivity such that this approach works perfectly even if an environment is unknown. In this context, we present a software implementation Fuzzy Logic FL path planning in a terrain. Fuzzy logic allows a continuum of control variables such as heading angles and speeds to be considered, as opposed to the discrete numbers used in crisp behaviors. Artificial intelligence, including Fuzzy logic has been actively studied and applied to domains such as automatically control of complex systems like robot. In f act, recognition, learning, decision-making, and action constitute the principal obstacle avoidance problems, so it is interesting to replace the classical approaches by technical approaches based on intelligent computing technologies. This technology FL is becoming useful as alternate approach to the classical techniques one. Also, fuzzy logic can be viewed as an attempt to bring together conventional precise mathematics and humanlike decision-making concepts. Fuzzy logic can be a valid approach solving control problem in a wide range of applications. To deal with the principle, the robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the unknown target. This algorithm provides the robot the possibility to move from the initial position to the final position (target) without collisions where the main factors of moving are included such as learning, deciding, acting, cognition, perception, and thinking. The robot succeeds to reach the target without collisions. The results gotten of the FL on randomly generated terrains are very satisfactory and promising. The extension of the FL for solving both paths planning and trajectory planning.