Free Flight

Free flight research focuses on developing autonomous control systems for unmanned aerial vehicles (UAVs) of various types, aiming for improved maneuverability, stability, and energy efficiency in diverse environments. Current research emphasizes model-free reinforcement learning techniques, such as deep reinforcement learning and model predictive control, alongside model-based approaches leveraging physics-informed neural networks and digital twins for enhanced simulation and control. These advancements are crucial for enabling safer and more efficient operation of UAVs in complex scenarios, including urban air mobility and autonomous aerial exploration, with applications ranging from package delivery to environmental monitoring.