Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Preprint Posting Date2017-08-19
Online Publication Date2019-01-29
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/626557
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AbstractAutomating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient on-board processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.
CitationMüller M, Casser V, Smith N, Michels DL, Ghanem B (2019) Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation. Physics of Solid Surfaces: 11–29. Available: http://dx.doi.org/10.1007/978-3-030-11012-3_2.
SponsorsThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.
JournalPhysics of Solid Surfaces
Conference/Event name15th European Conference on Computer Vision, ECCV 2018