  Unmapped areas and aerodynamic disturbances render autonomous navigation with
quadrotors extremely challenging. To fly safely and efficiently, trajectory
planners and trackers must be able to navigate unknown environments with
unpredictable aerodynamic effects in real-time. When encountering aerodynamic
effects such as strong winds, most current approaches to quadrotor trajectory
planning and tracking will not attempt to deviate from a determined plan, even
if it is risky, in the hope that any aerodynamic disturbances can be resisted
by a robust controller. This paper presents a novel systematic trajectory
planning and tracking framework for autonomous quadrotors. We propose a
Kinodynamic Jump Space Search (Kino-JSS) to generate a safe and efficient route
in unknown environments with aerodynamic disturbances. A real-time Gaussian
Process is employed to model the effects of aerodynamic disturbances, which we
then integrate with a Model Predictive Controller to achieve efficient and
accurate trajectory optimization and tracking. We demonstrate our system to
improve the efficiency of trajectory generation in unknown environments by up
to 75\% in the cases tested, compared with recent state-of-the-art. We also
demonstrate that our system improves the accuracy of tracking in selected
environments with unpredictable aerodynamic effects.
