Deep learning based dynamic UAV positioning system design

Existing UAV positioning methods are often static or rule-based, which limits their ability to respond quickly to changing environmental conditions and user demands. This thesis aims to develop a deep learning based dynamic positioning system for Unmanned Aerial Vehicles (UAVs) in dynamic environments. A Deep Learning algorithm will be used to optimize UAV coverage and improve response times. The system will be designed to perform effectively in scenarios where dynamic adaptation is critical, such as disaster response, surveillance and communication networks. The simulation model will continuously optimize the position of UAVs in real-time based on user demands and environmental factors. The performance of the system will be evaluated based on criteria such as coverage, response time and adaptability in dynamic conditions. This research aims to overcome the limitations of static and rule-based approaches in the literature and provide a more effective UAV positioning system using deep learning techniques.