Fixed-wing unmanned aerial vehicles (UAVS) pose advantages in energy efficiency, endurance, and speed, but also pose disadvantages in maneuverability. These maneuverability challenges can be addressed by exploiting high angle of attack maneuvers. However, navigation with fixed-wing UAVs in constrained spaces is still extremely difficult when the system state and environment are unknown.
This essay investigates the use of vision sensors in autonomous navigation of aerobatic fixed-wing UAVs. Perception aware NMPC is explored through the integration of a visibility metric into the trajectory optimization problem. Additionally, a novel frontier-based NMPC method, which improves obstacle avoidance capabilities while mapping, is proposed. These methods are evaluated in a realistic real-time simulation
