Unmanned Arial Vehicle

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across diverse applications, driving research focused on enhancing their safety, reliability, and operational efficiency. Current research emphasizes developing advanced data-driven systems for real-time fault detection and localization using machine learning techniques like deep neural networks (including LSTMs and CNNs) and hybrid models combining simulation and real-world data. These advancements, along with the integration of technologies such as Neural Radiance Fields for 3D environment mapping and reinforcement learning for optimized task scheduling, aim to improve UAV dependability and expand their capabilities in areas like disaster response and intelligent transportation systems.