Turkish Air Force Academy

About: Turkish Air Force Academy is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Genetic algorithm & Fuzzy logic. The organization has 162 authors who have published 369 publications receiving 5278 citations. The organization is also known as: Air Warfare School & National Defense University Air Force Academy.

Flying Ad-Hoc Networks (FANETs)

Abstract: One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges

Abstract: In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.

Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

Abstract: In this paper, an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described. To control the position of the UAV in three dimensional space as altitude and longitude-latitude location, three fuzzy logic modules are developed. These adjust the pitch angle, the roll angle and the throttle position of the UAV so that its altitude, the heading and the speed are controlled together. The implementation framework utilizes MATLAB's standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. To demonstrate the performance and potential of the controllers, the Aerosonde UAV model is used. Flight Gear open source flight simulator and Gauges Block Set are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Steep turn maneuvers which are used for basic training of pilots are applied to test the performance of the fuzzy logic controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.

A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy and Neural Networks

Abstract: Particle swarm optimization (PSO), a relatively new population-based intelligence algorithm, exhibits good performance on optimization problems. However, during the optimization process, the particles become more and more similar, and gather into the neighborhood of the best particle in the swarm, which makes the swarm prematurely converged most likely around the local solution. A new optimization algorithm called multifrequency vibrational PSO is significantly improved and tested for two different test cases: optimization of six different benchmark test functions and direct shape optimization of an airfoil in transonic flow. The algorithm emphasizes a new mutation application strategy and diversity variety, such as global random diversity and local controlled diversity. The results offer insight into how the mutation operator affects the nature of the diversity and objective function value. The local controlled diversity is based on an artificial neural network. As far as both the demonstration cases' problems are considered, remarkable reductions in the computational times have been accomplished.