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Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness

Neamati, Daniel and Nakka, Yashwanth Kumar K. and Chung, Soon-Jo (2022) Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness. In: AIAA SCITECH 2022 Forum. American Institute of Aeronautics and Astronautics , Reston, VA, Art. No. 2022-2271. ISBN 978-1-62410-631-6. https://resolver.caltech.edu/CaltechAUTHORS:20220210-928467000

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Abstract

Accurate gravity field models are essential for safe proximity operations around small bodies. State-of-the-art techniques use spherical harmonics or high-fidelity polyhedron shape models. Unfortunately, these techniques can become inaccurate near the surface of the small body or have high computational costs, especially for binary or heterogeneous small bodies. New learning-based techniques do not encode a predefined structure and are more versatile. In exchange for versatility, learning-based techniques can be less robust outside the training data domain. In deployment, the spacecraft trajectory is the primary source of dynamics data. Therefore, the training data domain should include spacecraft trajectories to accurately evaluate the learned model's safety and robustness. We have developed a novel method for learning-based gravity models that directly uses the spacecraft's past trajectories. We further introduce a method to evaluate the safety and robustness of learning-based techniques via comparing accuracy within and outside of the training domain. We demonstrate this safety and robustness method for two learning-based frameworks: Gaussian processes and neural networks. Along with the detailed analysis provided, we empirically establish the need for robustness verification of learned gravity models when used for proximity operations.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.2514/6.2022-2271DOIArticle
https://doi.org/10.2514/6.2022-2271.vidDOIVideo Presentation
https://arxiv.org/abs/2112.09998arXivDiscussion Paper
ORCID:
AuthorORCID
Neamati, Daniel0000-0002-1555-1433
Nakka, Yashwanth Kumar K.0000-0001-7897-3644
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2022 American Institute of Aeronautics and Astronautics.
Group:GALCIT
Other Numbering System:
Other Numbering System NameOther Numbering System ID
AIAA Paper2022-2271
DOI:10.2514/6.2022-2271
Record Number:CaltechAUTHORS:20220210-928467000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220210-928467000
Official Citation:Daniel Neamati, Yashwanth Kumar K. Nakka and Soon-Jo Chung. "Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness," AIAA 2022-2271. AIAA SCITECH 2022 Forum. January 2022. https://doi.org/10.2514/6.2022-2271
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113397
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:10 Feb 2022 20:29
Last Modified:10 Feb 2022 22:11

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