  Quadrotors can achieve aggressive flight by tracking complex maneuvers and
rapidly changing directions. Planning for aggressive flight with trajectory
optimization could be incredibly fast, even in higher dimensions, and can
account for dynamics of the quadrotor, however, only provides a locally optimal
solution. On the other hand, planning with discrete graph search can handle
non-convex spaces to guarantee optimality but suffers from exponential
complexity with the dimension of search. We introduce a framework for
aggressive quadrotor trajectory generation with global reasoning capabilities
that combines the best of trajectory optimization and discrete graph search.
Specifically, we develop a novel algorithmic framework that
\textit{interleaves} these two methods to complement each other and generate
trajectories with provable guarantees on completeness up to discretization. We
demonstrate and quantitatively analyze the performance of our algorithm in
challenging simulation environments with narrow gaps that create severe
attitude constraints and push the dynamic capabilities of the quadrotor.
Experiments show the benefits of the proposed algorithmic framework over
standalone trajectory optimization and graph search-based planning techniques
for aggressive quadrotor flight.
