Autonomous Aerial Navigation

Autonomous aerial navigation focuses on enabling unmanned aerial vehicles (UAVs) to navigate and perform tasks without human intervention, primarily addressing safe and efficient obstacle avoidance and precise target acquisition. Current research emphasizes robust perception using diverse sensor fusion (e.g., vision, LiDAR, IMU) and advanced algorithms like deep reinforcement learning (including variations such as SAC and PPO), graph-based transformers, and Bayesian deep learning for uncertainty quantification. These advancements are crucial for expanding UAV applications in diverse fields, including delivery, inspection, and search and rescue, by improving reliability and safety in complex and dynamic environments.