Title
Interleaving Graph Search And Trajectory Optimization For Aggressive Quadrotor Flight
Abstract
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.
Year
DOI
Venue
2021
10.1109/LRA.2021.3067298
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Aerial systems: applications, autonomous vehicle navigation, graph search, motion and path planning, trajectory optimization
Journal
6
Issue
ISSN
Citations 
3
2377-3766
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Ramkumar Natarajan101.35
Howie Choset22826257.12
Maxim Likhachev32103157.03