Unmanned Aerial Vehicles (UAVs) are defined by the United States Department of Defense (DOD) as powered, aerial vehicles that do not carry a human operator. UAVs control systems have assumed an increasingly important role in the development and advancement of modern civilization and technology. In recent years, intelligent control of autonomous navigation of UAVs has gained much interest mainly: neural networks, non-linear adaptive control, fuzzy logic as well as combinations of these methods, such as, neuro—fuzzy control, or fuzzy logic and evolutionary or genetic algorithms. The purpose of this paper is to develop an autonomous UAV fuzzy Logic navigation and obstacle avoidance controller that is capable to perform a way point navigation and obstacles avoidance along its flight. The navigation and obstacle avoidance behavior is transformed via fuzzy rules. Multi-platform simulation package is developed to perform closed loop on-line environment sensing. Several simulation tests have been conducted on the new controller. These tests show that the proposed controller achieves the desired performance with respect to its sensors capability and limitations.