Optimization and performance analysis of Drones and Unmanned Aerial Systems and Their Intelligence Applications

• The study highlighted the main principles of autonomous navigation and its mathematical aspects. • The study presented an optimization model for path loss modelling and provided a comparative analysis of the state-of-the-art models. • The study presented the state-of-the-art algorithms and approaches in UAVs. Advancements in unmanned aerial systems (UAS) and artificial intelligence (AI) have emerged in recent years, which have accelerated research in a variety of fields, including human-drone interaction, autonomous navigation, security, object detection, urban air mobility, energy-efficient design, environmental monitoring, archaeological research, wildlife conservation, medical supply delivery, disaster response, and precision agriculture. These represent just a few areas where AI-driven drones are making an impact in society. The study discussed the main principles of autonomous navigation and its mathematical aspects and presented the state-of-the-art algorithms in unmanned aerial vehicles (UAVs). The study also demonstrated an optimization model for path loss considering terrain and environmental constraints and provided a comparative analysis of the state-of-the-art models. This study contributes to the ever-expanding field of drone technology by addressing critical research gaps and introducing novel approaches, but also offers practical recommendations for professionals and policymakers. The findings of this study pave the way for the integration of UAS into various applications, fostering the adoption of innovative solutions. Additionally, this research has the potential to serve as a foundational reference for future studies, encouraging collaboration and driving advancements in drone technology.