Deep Learning Based Communication and Control of Swarm Unmanned Aerial Vehicles

With the developing technology, studies on unmanned systems have been increasing rapidly in recent years. The most used area of unmanned systems is unmanned aerial vehicles (UAV). UAV systems are reduced to small sizes and used in swarms to have more effective capabilities. Swarm UAVs are used in many areas such as agriculture, natural disasters, forest fires, air pollution detection and defense industry. In this study, the use of swarm UAVs in line with military purposes is discussed. For UAVs used in swarms, it is aimed to benefit from artificial intelligence solutions, to provide communication and control, and to create a system with full autonomy. In order to carry out this study, the system covering the autonomy levels, communication systems, control architectures and control algorithms has been trained with the Particle Swarm Optimization (PSO) Algorithm of Artificial Neural Networks (ANN). In this study, it is aimed to develop a swarm UAV system with deep learning and machine learning methods with full autonomy, high awareness and detect/avoid system, and to develop artificial intelligence and swarm UAV technology.