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Cislunar Trajectory Design and Maneuver Autonomy for NASA's Moon to Mars ArchitectureNASA’s Moon to Mars architecture is an ambitious roadmap of manned cislunar and deep space exploration. The extensive amount of orbital assets required will place a significant burden on ground-based resources, such as communication networks and operations facilities. Spacecraft autonomy is essential for maintaining a vast number of complex missions beyond Earth orbit. To achieve full autonomy, spacecraft must be able to employ methods of robust maneuver design without an explicit dependence on commands sent from the ground. This level of autonomy is needed not only for stationkeeping, but also for outbound transfers. To address the need of spacecraft maneuver design autonomy, this work investigates the use of neural networks (NNs) in a supervised learning environment. A supervised learning approach for NNs allows for a curated training data set, consisting exclusively of perturbations applied to a desired mission concept of operations (ConOps). The proposed approach allows humans on the ground to design a specific mission ConOps before flight, then employ NNs to fly the mission robustly and autonomously. This investigation numerically tests maneuver autonomy in four highly sensitive regions of flight: orbit raising, translunar injection burns, powered lunar flybys, and invariant manifold insertion burns. These straining cases are contextualized by testing them in a demonstration mission, targeting an Earth-Moon L3 orbit. The study first establishes feasibility by automating impulsive burn maneuvers. However, some guidance algorithms will need more intensive commands, such as inertial pointing and angular rates. To validate this method, NN maneuver autonomy is applied to a finite burn model of the demonstration mission. The use of sequential, mission specific maneuvers provide an appropriate testbed to demonstrate the robustness of a NN trained on feasible perturbed states. Moreover, these scenarios provide preliminary proof-of-concept for fully autonomous missions that execute maneuvers without dependence upon explicit command uplinks. As a result, the technological advancement proposed in this work may significantly ease the strain on ground-based mission operations. This would enable complex and autonomous mission execution in cislunar and deep space regimes, filling a technology gap required to support future manned missions.
Document ID
20240011707
Acquisition Source
Marshall Space Flight Center
Document Type
Conference Paper
Authors
Aaron Houin
(Marshall Space Flight Center Redstone Arsenal, United States)
Rohan Sood
(University of Alabama Tuscaloosa, United States)
Date Acquired
September 12, 2024
Subject Category
Astrodynamics
Report/Patent Number
IAC–24–C1.7.4.83115
Meeting Information
Meeting: 75th International Astronautical Congress (IAC)
Location: Milan
Country: IT
Start Date: October 14, 2024
End Date: October 18, 2024
Sponsors: International Astronautical Federation (IAF)
Funding Number(s)
WBS: 954879.02.06.62
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Technical Management
Keywords
NASA
Moon2Mars
Cislunar
Maneuver
Autonomy
Neural Network
Supervised Learning
AI
Machine Learning
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