Low-thrust multiple asteroid missions with return to earth using machine learning

Viavattene, G. and Ceriotti, M. (2021) Low-thrust multiple asteroid missions with return to earth using machine learning. In: 2020 AAS/AIAA Astrodynamics Specialist Conference, 9-12 August 2020, AAS 20-645. ISBN 9780877036753

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Sample-return missions to near-Earth asteroids (NEAs) are invaluable for the scientific community to learn more about the initial stages of the solar system formation and life evolution. Thanks to its high specific impulse, a low-thrust propulsion technology is capable of performing multiple asteroid rendezvouses (to collect samples) and eventually returning to Earth. To identify the best asteroid sequences with return to Earth, this work proposes to employ machine learning techniques and, specifically, artificial neural networks (ANNs), to quickly estimate the cost of each transfer between asteroids. The ANN is integrated within a sequence search algorithm based on a tree search, which identifies the asteroid sequences and selects the best ones in terms of propellant mass required and interest value. This algorithm can design the sequences so that specific asteroids of interest, for which a sample return would be more valuable, can be targeted. A pseudospectral optimal control solver is then used to find the optimal trajectory and control history. The performance of the proposed methodology is assessed by analyzing three distinctive NEA sequences ending with return to Earth and rendezvous. A near-term low-thrust propulsion enables to rendezvous five asteroids, and ideally return samples to Earth in about ten years from launch. It is demonstrated that visiting more interesting asteroids from the scientific point of view increases the appeal of the sequence at the cost of a greater propellant mass required.

Item Type:Conference Proceedings
Additional Information:Online conference. 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
Glasgow Author(s) Enlighten ID:Ceriotti, Dr Matteo and Viavattene, Giulia
Authors: Viavattene, G., and Ceriotti, M.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright 2021 by American Astronautical society
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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