Design of multiple space debris removal missions using machine learning

Viavattene, G. , Devereux, E., Snelling, D., Payne, N., Wokes, S. and Ceriotti, M. (2022) Design of multiple space debris removal missions using machine learning. Acta Astronautica, 193, pp. 277-286. (doi: 10.1016/j.actaastro.2021.12.051)

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Active debris removal (ADR) allows for the disposal of inactive satellites and larger objects, preventing the build-up of space junk and allowing to replace aging agents in a constellation. To make ADR missions more commercially viable, the removal and disposal of multiple debris objects using a single spacecraft are investigated. This paper proposes the use of artificial neural networks (ANNs) to quickly estimate the cost and duration of the transfers to de-orbit a range of debris objects, so that it is possible to identify the optimal sequence of objects which minimizes the cost and/or the duration of the mission, for the maximum number of de-orbited objects. To this end, the ANN is integrated within a sequence search algorithm based on a tree search. The performance of the proposed methodology is assessed by analyzing three distinctive sequences of multiple space debris removals. A near-term low-thrust propulsion technology enables to dispose of up to 13 debris objects within 10 years, when the optimal design parameters are chosen. The use of ANN allows for this solution to be found 26 times faster than current methods, while enabling the selection of faster and less expensive (being the propellant mass required lower) options.

Item Type:Articles
Additional Information:Giulia Viavattene gratefully acknowledges the support received for this research from the James Watt School of Engineering at the University of Glasgow for funding the research under the James Watt sponsorship program. The authors acknowledge the United Kingdom Space Agency (UKSA), for supporting part of this work.
Keywords:Space debris, debris removal, artificial neural network, machine learning, astrodynamics, low thrust.
Glasgow Author(s) Enlighten ID:Viavattene, Giulia and Ceriotti, Dr Matteo
Authors: Viavattene, G., Devereux, E., Snelling, D., Payne, N., Wokes, S., and Ceriotti, M.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Acta Astronautica
ISSN (Online):1879-2030
Published Online:17 January 2022
Copyright Holders:Copyright © 2022 IAA
First Published:First published in Acta Astronautica 193: 277-286
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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