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Completed TRL 5 (started at 3, targeting 5)
The objective of this study is to develop an autonomous maneuvering scheme using on-board Artificial Intelligence (AI) for an Earth Science-based SmallSat Distributed Space Mission (DSM). This research study will result in the development of a software architecture, which uses machine-learning algorithm(s), to enable autonomous decision-making for performing maneuvers. Decision-making will be based on observations from a DSM orbit constellation. A trade study will be performed to determine an optimal DSM orbit constellation design for a future NASA Earth Science mission concept.
This study will help to: identify an adaptive architecture to enable spacecraft-to-spacecraft communication for SmallSats, enable autonomous decision-making based on observations for SmallSat DSMs, and foster a means to achieve desired SmallSat formation via an innovative maneuvering scheme.
In addition, this study fits well with autonomous Navigation, Guidance, and Control (autoNGC) efforts at NASA GSFC. Current autoNGC applications can provide GPS-based navigation for Earth Science missions such as the one in this IRAD study. Then, machine-learning algorithm(s) we develop can be used to determine guidance goals for DSM constellations. Moreover, coupling this effort with upcoming autoNGC optical navigation (OpNav) capabilities will enable future deep space DSMs.
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