CEAS EuroGNC 2022
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Integrated Updraft Localization and Exploitation: End-to-End Type Reinforcement Learning Approach
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| Stefan Notter |
Research Associate, Institute of Flight Mechanics and Controls, University of Stuttgart, 70569, Stuttgart, Germany. | | Gregor Müller |
Graduate Student, Institute of Flight Mechanics and Controls, University of Stuttgart, 70569, Stuttgart, Germany. | | Walter Fichter |
Professor, Institute of Flight Mechanics and Controls, University of Stuttgart, 70569, Stuttgart, Germany. |
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| Abstract:
Autonomous soaring constitutes an appealing academic sample problem for investigating machine learning methods within the scope of aerospace guidance, navigation, and control. The stochastic nature of small-scale meteorological phenomena renders the task of localizing and exploiting thermal updrafts suited for applying a reinforcement learning approach. Within this work, we present a training setup for learning an integrated control strategy for autonomous localization and exploitation of thermal updrafts. In particular, we propose a deep artificial neural network featuring a Long Short-Term Memory to represent the policy. Instead of just implementing a static control law, the recurrent structure facilitates observability and enables mapping the hard-to-model dynamics of thermal updrafts. The end-to-end type control policy integrates an estimator for updraft localization, including a latent state-transition model. We show in simulation, that the trained agent autonomously localizes and exploits stochastic, non-stationary thermal updrafts. The unaltered reinforcement learning setup can be deployed to further improve the control policy through real-world interactions.
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| Keywords: Autonomous Soaring; Intelligent Systems; Artificial Intelligence; Reinforcement Learning; Long Short-Term Memory; End-to-End Learning; Integrated Filtering and Control |
 View PDF CEAS-GNC-2022-077 |
| Stefan Notter, Gregor Müller, Walter Fichter: Integrated Updraft Localization and Exploitation: End-to-End Type Reinforcement Learning Approach. Proceedings of the 2022 CEAS EuroGNC conference. Berlin, Germany. May 2022. CEAS-GNC-2022-077.
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| BibTeX entry:
@Incollection{CEAS-GNC-2022-077,
authors = {Notter, Stefan and Müller, Gregor and Fichter, Walter},
title = {Integrated Updraft Localization and Exploitation: End-to-End Type Reinforcement Learning Approach},
booktitle = {Proceedings of the 2022 {CEAS EuroGNC} conference},
address = {Berlin, Germany},
month = may,
year = {2022},
note = {CEAS-GNC-2022-077}
}
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