EAPSI: A Machine Learning Approach to Lunar Spacecraft Trajectory Optimization

Sprague Christopher I, Troy NY

Investigators

Abstract

This research will investigate innovative spacecraft trajectory optimization methods at the Japanese Aerospace Exploration Agency (JAXA) in collaboration with Dr. Yasuhiro Kawakatsu for the upcoming lunar small-spacecraft mission, EQUULEUS, which will be launched aboard NASA's Space Launch System (SLS) rocket at the end of 2018. The findings may enable space missions that were once thought to be impossible, leading to more exotic and exciting opportunities for science collection. This mission will also further scientists understanding of the radiation environment surrounding Earth by imaging its plasmasphere and measuring its distribution, which may provide important insight for protecting both humans and electronics from radiation damage during long space journeys. This research explores transformative concepts, combining machine learning and trajectory optimization, two subjects which, in combination, have been largely unexplored. Collaboration with JAXA is a unique opportunity to further this research, as it is a recognized trajectory design leader and has extensive mission experience with low-thrust and low-energy spacecraft. The spacecraft will insert itself into a stable orbit about the L2 Lagrange point of the Earth-Moon system through a cislunar trajectory, exploiting the topological stability of the Earth-Moon system's effective potential through low-energy pathways. A large data set of optimal control trajectories will be generated through conventional trajectory optimization methods (i.e. direct methods and indirect methods). Once the data set of state-control pairs is generated, an artificial neural network (ANN) will be trained on the data set. Through training, the ANN develops a spatial control policy that can be implemented in real-time. The spacecraft, at any moment in time, will perceive its environment and take actions (i.e. throttle its thrusters) accordingly. This control method is analogous to how organisms behave in nature. Just as a simple house fly is able to navigate to its food source, making decisions in real-time, a spacecraft should be able to do the same when trying to achieve its objective. This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.