NSF Org:	OISE Office of International Science and Engineering
Recipient:
Initial Amendment Date:	May 8, 2017
Latest Amendment Date:	May 8, 2017
Award Number:	1713973
Award Instrument:	Fellowship Award
Program Manager:	Anne Emig OISE  Office of International Science and Engineering O/D  Office Of The Director
Start Date:	June 1, 2017
End Date:	May 31, 2018 (Estimated)
Total Intended Award Amount:	$5,400.00
Total Awarded Amount to Date:	$5,400.00
Funds Obligated to Date:	FY 2017 = $5,400.00
History of Investigator:	Christopher Sprague (Principal Investigator)
Recipient Sponsored Research Office:	Sprague Christopher I Troy NY  US  12180-3599
Sponsor Congressional District:
Primary Place of Performance:	Japan Aerospace Exploration Agency Sagamihara, Kanagawa 252-5  JA
Primary Place of Performance Congressional District:
Unique Entity Identifier (UEI):
Parent UEI:
NSF Program(s):	EAPSI
Primary Program Source:	01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	5921, 5978, 7316
Program Element Code(s):	731600
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.079

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.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project investigated innovative spacecraft trajectory optimization methods at the Japan Aerospace Exploration Agency (JAXA) in collaboration with Dr. Yasuhiro Kawakatsu for the lunar spacecraft mission EQUULEUS, to be launched aboard NASA's Space Launch System (SLS) rocket at the end of 2018.
With consideration to the computational burden entailed by conventional spacecraft trajectory optimization techniques, this project researched the merit of using machine learning techniques to to enable real-time autonomous trajectory optimization with the goal to enable space missions of increasing complexity, thus allowing for more exotic scientific data collection and hence a greater understanding of the universe surrounding us. In addition, with greater trajectory optimization capabilities, the ability to develop increasingly complex space infrastructure frameworks increases, thus benefiting the integrity of society's operation as a whole.
During the course of the project, preexisting conventional spacecraft trajectory optimization techniques were improved upon in several ways. The researched method reduced the dimensionality of the trajectory optimization problem, reducing its computational complexity. Secondly, the method yields a trajectory optimization solution of greater detail. Lastly and most importantly, the method yields a trajectory controller, in the form of a trained neural network, which can be used in real-time with robust generalization capabilties to compute optimal controls, thus circumventing the aforementioned computational burden entailed by conventional spacecraft trajectory optimization techniques.
This research suggests that the use of machine learning does indeed hold merit in the context of spacecraft trajectory optimization, two subjects which have only rarely been researched in tandem. As machine learning has increasingly been included in a number of disciplines with notable benefits, it is hoped that this research will drive this inclusion into the space exploration industry with synonymous benefits.

Last Modified: 06/01/2018
Modified by: Christopher I Sprague

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