Modeling Irregular Small Bodies Gravity Field Via Extreme Learning Machines and Bayesian Optimization
Name
ASR__Small_bodies_gravity_field_FinalPub-1.pdf
Description
Accepted version
Size
9.92 MB
Format
Adobe PDF
Checksum (MD5)
dd621ddc270e84aeb8f19a235266e309
Author(s)
Furfaro, Roberto
Barocco, Riccardo
Linares, Richard
Topputo, Francesco
Reddy, Vishnu
Simo, Jules
Le Corre, Lucille
Date Issued
2021
Journal
Advances in Space Research
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ASR.2020.06.021
