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Machine learning of optimal low-thrust transfers between near-earth objects

Machine learning of optimal low-thrust transfers between near-earth objects
Machine learning of optimal low-thrust transfers between near-earth objects

During the initial phase of space trajectory planning and optimization, it is common to have to solve large dimensional global optimization problems. In particular continuous low-thrust propulsion is computationally very intensive to obtain optimal solutions. In this work, we investigate the application of machine learning regressors to estimate the final spacecraft mass mf after an optimal low-thrust transfer between two Near Earth Objects instead of solving the corresponding optimal control problem (OCP). Such low thrust transfers are of interest for several space missions currently being developed such as NASA’s NEA Scout. Previous work has shown machine learning to greatly improve the estimation accuracy in the case of short transfers within the main asteroid belt. We extend this work to cover also the more complicated case of multiple-revolution transfers in the near Earth regime. In the process, we reduce the general OCP of solving for mf to a much simpler OCP of determining the maximum initial spacecraft mass m for which the transfer is feasible. This information, along with readily available information on the orbit geometries, is sufficient to learn the final mass mf for the same transfer starting with any initial mass mi. This results in a significant reduction of the computational cost compared to solving the full OCP.

Astrodynamics, Low thrust transfers, Machine learning, Near earth Objects, Regression
0302-9743
543-553
Springer
Mereta, Alessio
043ec19d-fbe6-4a6d-9c7c-3c9e5d02d0bf
Izzo, Dario
f2f4a956-4193-4150-9734-2afc0544be6b
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Martinez de Pison, F.
Urraca, R.
Quintian, H.
Corchado, E.
Mereta, Alessio
043ec19d-fbe6-4a6d-9c7c-3c9e5d02d0bf
Izzo, Dario
f2f4a956-4193-4150-9734-2afc0544be6b
Wittig, Alexander
3a140128-b118-4b8c-9856-a0d4f390b201
Martinez de Pison, F.
Urraca, R.
Quintian, H.
Corchado, E.

Mereta, Alessio, Izzo, Dario and Wittig, Alexander (2017) Machine learning of optimal low-thrust transfers between near-earth objects. Martinez de Pison, F., Urraca, R., Quintian, H. and Corchado, E. (eds.) In Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings. vol. 10334, Springer. pp. 543-553 . (doi:10.1007/978-3-319-59650-1_46).

Record type: Conference or Workshop Item (Paper)

Abstract

During the initial phase of space trajectory planning and optimization, it is common to have to solve large dimensional global optimization problems. In particular continuous low-thrust propulsion is computationally very intensive to obtain optimal solutions. In this work, we investigate the application of machine learning regressors to estimate the final spacecraft mass mf after an optimal low-thrust transfer between two Near Earth Objects instead of solving the corresponding optimal control problem (OCP). Such low thrust transfers are of interest for several space missions currently being developed such as NASA’s NEA Scout. Previous work has shown machine learning to greatly improve the estimation accuracy in the case of short transfers within the main asteroid belt. We extend this work to cover also the more complicated case of multiple-revolution transfers in the near Earth regime. In the process, we reduce the general OCP of solving for mf to a much simpler OCP of determining the maximum initial spacecraft mass m for which the transfer is feasible. This information, along with readily available information on the orbit geometries, is sufficient to learn the final mass mf for the same transfer starting with any initial mass mi. This results in a significant reduction of the computational cost compared to solving the full OCP.

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More information

e-pub ahead of print date: 2 June 2017
Published date: 2017
Venue - Dates: 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017, , La Rioja, Spain, 2017-06-21 - 2017-06-23
Keywords: Astrodynamics, Low thrust transfers, Machine learning, Near earth Objects, Regression

Identifiers

Local EPrints ID: 419789
URI: http://eprints.soton.ac.uk/id/eprint/419789
ISSN: 0302-9743
PURE UUID: f600da55-ed78-496a-a132-90a528569ab7
ORCID for Alexander Wittig: ORCID iD orcid.org/0000-0002-4594-0368

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Date deposited: 20 Apr 2018 16:30
Last modified: 06 Jun 2024 01:59

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Contributors

Author: Alessio Mereta
Author: Dario Izzo
Editor: F. Martinez de Pison
Editor: R. Urraca
Editor: H. Quintian
Editor: E. Corchado

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