Integration of Artificial Intelligence into Unmanned Aerial Vehicles

In this study, the effects of artificial intelligence (AI) technologies on the flight control, motion dynamics, environmental perception, energy management, and autonomous mission execution capabilities of unmanned aerial vehicles (UAVs) are comprehensively analyzed. The integration of AI subfields machine learning, deep learning, fuzzy logic, and reinforcement learning into UAV systems is examined, and the advantages these methods offer compared to classical control systems are evaluated. The role of data processing, sensor fusion, and decision support mechanisms within modern UAV architectures is discussed in detail. Three different AI based approaches computational intelligence, fuzzy logic based control, and reinforcement learning based control are compared within the scope of this study. The findings indicate that genetic algorithms outperform other methods in route planning, fuzzy logic provides superior performance in environments with uncertainty and dynamic conditions, and reinforcement learning excels in fully autonomous control and adaptive learning. Owing to this methodological diversity, UAV behaviors were analyzed from multiple perspectives, including optimization, uncertainty management, and learning based control. The results reveal that AI driven methods significantly enhance adaptive decision making abilities in UAV operations, allow more effective management of environmental uncertainties, and substantially improve mission success rates. In light of these findings, hybrid AI control systems are evaluated as the most promising architectural solution for next generation UAV design. This study presents the technical, theoretical, and practical contributions of AI to UAV technologies and provides an integrated framework for the development of future fully autonomous UAV systems.