Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks
نویسندگان
چکیده
Asteroid exploration has been attracting more attention in recent years. Nevertheless, we have just visited tens of asteroids while discovered than one million bodies. As our current observation and knowledge should be biased, it is essential to explore multiple directly better understand the remains planetary building materials. One mission design solutions utilizing asteroid flyby cycler trajectories with Earth gravity assists. An trajectory problem a subclass global optimization problems flybys, involving for given sequence combinatorial decide flybys. number bodies grows, computation time this expands maliciously. This paper presents new method surrogate model constructed by deep neural networks approximating results. Since bottlenecks machine learning approaches generate massive databases, propose an efficient database generation strategy introducing pseudo-asteroids satisfying Karush-Kuhn-Tucker conditions. The numerical result applied JAXA's DESTINY+ shows that proposed practically applicable space can significantly reduce computational searching sequences.
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ژورنال
عنوان ژورنال: Journal of Guidance Control and Dynamics
سال: 2022
ISSN: ['1533-3884', '0731-5090']
DOI: https://doi.org/10.2514/1.g006487