Low-Thrust, Multiple-Near-Earth-Asteroid Mission Design with Sample Return to Earth Using Machine Learning

Abstract

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. Low-thrust propulsion technology enables missions with multiple-asteroid rendezvouses to collect samples and eventually return to Earth, thanks to its high specific impulse. 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 NEA 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. Near-term low-thrust propulsion enables to rendezvous five asteroids, and return samples to Earth in about 10 years from launch. It is demonstrated that visiting more scientifically interesting asteroids increases the appeal of the sequence at the cost of more propellant mass required.