Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Automating 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.
Original languageEnglish (US)
Title of host publicationPhysics of Solid Surfaces
PublisherSpringer Nature
Pages11-29
Number of pages19
ISBN (Print)9783030110116
DOIs
StatePublished - Jan 29 2019

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