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2019 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Computer Vision – ECCV 2018 Workshops

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
verfasst von
Matthias Müller
Vincent Casser
Neil Smith
Dominik L. Michels
Bernard Ghanem
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-11012-3_2