Trajectory Estimation for GeoFencing Applications on SmallSize FixedWing UAVs The steadily increasing popularity of Unmanned Aerial Vehicles (UAVs) is creating new opportunities in diverse fields of technology and business. However, this increase of popularity also raises safety concerns. To tackle the primary concern of keeping the UAV inside a designated region, a novel trajectory estimation algorithm for geofencing applications is proposed. We derive the BetaTrajectory that takes into account constraints in curvature as well as constraints in the change of curvature which is bounded by the maximum rollrate of the aircraft. We incorporate the BetaTrajectory into a geofencing algorithm. By using our opensource uavAP autopilot, the applicability and necessity of accurate trajectory estimation algorithms for geofencing applications are shown on small fixedwing aircraft. The model and algorithm are validated in highfidelity simulations as well as in real flight testing.

An Autonomous Quadrotor System for Robust HighSpeed Flight Through Cluttered Environments Without GPS Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other applications require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while highspeed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two widefieldofview cameras for visualinertial motion tracking and relocalization to a prior map. Collisionfree trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotorspecific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 ms and vertical flights at up to 3.1 ms compared to the stateoftheart, and 3) demonstrate our system tracking complex, aggressive, trajectories.