  Many air-taxi concepts are capable of vertical takeoff and landing, enabling
them to fly to and from urban locations. An important capability for these air
taxis is the transition between hover and forward flight. We propose a robust
methodology for computing optimal takeoff and transition trajectories using
surrogate models trained on data from physics-based models. The use of
surrogate models reduces the computational complexity and improves the
robustness of the trajectory optimization algorithm. We demonstrate the
versatility and robustness of the proposed methodology by applying it to 12
trajectory optimization problems that involve air-taxi takeoff and outbound
transition. These trajectories are representative of real air-taxi operations,
with a variety of constraints derived, in part, from proposed mission
requirements.
