Optimized Route Planning and Precise Circle Detection in Unmanned Aerial Vehicle with Machine Learning

This research paper presents a distributed architecture for optimized route planning and precise circle detection in unmanned aerial vehicle with machine learning. The architecture focuses on three key areas: motion planning, control, and the integration of a web application with machine learning (ML) for autonomous drones. By leveraging advanced planning and control algorithms, the architecture enables UAVs to navigate dynamic environments, execute complex maneuvers, and maintain stability. The ML-integrated web application enhances decision-making for detection, optimizing route planning. Extensive simulations and real-world experiments validate the effectiveness and scalability of the proposed architecture, making it a valuable tool for advancing research in autonomous UAV systems.