Interleaving Graph Search and Trajectory Optimization for Aggressive Quadrotor Flight
This addresses the need for efficient and globally optimal trajectory planning for quadrotors in aggressive flight scenarios, though it is an incremental improvement over existing methods.
The paper tackles the problem of planning aggressive quadrotor flight by combining trajectory optimization and graph search to handle non-convex spaces and dynamics, resulting in trajectories with provable completeness guarantees and improved performance in challenging simulations.
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 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.