Autonomous Unmanned Aerial Vehicle

Autonomous unmanned aerial vehicles (UAVs) are being developed for a wide range of applications, focusing on safe and efficient navigation in diverse environments, including GPS-denied spaces and those with dynamic obstacles. Current research emphasizes robust control algorithms, such as model predictive control and deep reinforcement learning (including Proximal Policy Optimization and Deep Q-Networks), often coupled with advanced sensor fusion and efficient deep neural network architectures (e.g., transformers, MobileNetV3) for perception and decision-making. This research is significant for advancing both fundamental robotics and AI, and for enabling practical applications in search and rescue, infrastructure inspection, precision agriculture, and environmental monitoring.