Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight

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