Autonomous Landing

Autonomous landing systems for unmanned aerial vehicles (UAVs) aim to enable safe and precise landings in diverse and challenging environments, ranging from offshore platforms to uneven terrain. Current research heavily utilizes reinforcement learning (RL), particularly deep RL algorithms like Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), often coupled with computer vision techniques (e.g., object detection, semantic segmentation) for environment perception and model predictive control (MPC) for trajectory optimization. These advancements are crucial for expanding UAV applications in various sectors, including search and rescue, delivery, and infrastructure inspection, by enhancing safety and operational efficiency.