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Autonomous Systems for Space Exploration


Focus Area 3: Autonomous Systems for Space Exploration


Participating MD(s): SMD, STTR


The exploration of space requires the best of the nation's technical community to provide the technologies that will enable human and robotic exploration beyond Low Earth Orbit (LEO): to visit asteroids, to extend man’s reach to Mars, and for increasingly ambitious robotic missions such as a Europa Lander. Autonomous Systems technologies provide the means of migrating mission control from Earth to spacecraft, habitats, and robotic explorers. This is enhancing for missions in the Earth-Lunar neighborhood and enabling for deep space missions. Long light-time delays, for example up to 42 minutes round-trip between Earth and Mars, require time-critical control decisions to be closed on-board autonomously through automation, augmented for human missions by astronaut-automation teaming,

rather than through round-trip communication to Earth mission control.

Long-term crewed spacecraft and habitats, such as the International Space Station, are so complex that a significant portion of the crew's time is spent keeping it operational even under nominal conditions in low-Earth orbit, while still requiring significant real-time support from Earth. The considerable challenge is to migrate the knowledge and capability embedded in current Earth mission control, with tens to hundreds of human specialists ready to provide instant knowledge, to on-board automation that teams with astronauts to autonomously manage spacecraft and habitats. For outer planet robotic explorers, the opportunity is to autonomously and rapidly respond to dynamic environments in a timely fashion. The autonomous agent subtopic addresses this challenge by soliciting proposals that leverage the growing field of cognitive computing to advance technology for deep-space autonomy.




The technology challenge for autonomous crewed systems in off-nominal conditions is even more critical. In the majority of Apollo lunar missions, Earth mission control was needed to resolve critical off-nominal situations ranging from unexplained computer alarms on Apollo 11 to the oxygen tank explosion on Apollo 13 that required executing an 87-hour free return abort trajectory around the moon and back to Earth. Through creative use of Lunar Module assets, Apollo 13 had sufficient resiliency to keep the three astronauts alive despite loss of the oxygen tank and many of the capabilities of the service module. In contrast to a lunar mission, a free return abort trajectory around Mars and back to Earth is on the order of two years – requiring a leap in resiliency.  To prevent Loss of Mission (LOM) or Loss of Crew (LOC) in deep space missions, spacecraft and habitats will require long-term resiliency to handle failures that lead to loss of critical function or unexpected expenditure of consumables. Long communication delays or accidents that cause loss of communication will require that the initial failure response be handled autonomously. The subtopic on resilient autonomous systems solicits technology for the design and quantification of resiliency in long-duration missions. The subtopic on sustainable habitats solicits technology for long-term system health management that goes beyond short-term diagnosis technology to include advances machine learning and other prognostic technologies.
Enhancing the capability of astronauts is also critical for future long-duration deep space missions. Augmented reality technology can guide astronauts in carrying out procedures through various sensory modalities. The augmented reality subtopic within the STMD Robotics area is very relevant to autonomous systems technologies, and

proposers are encouraged to review that subtopic description.

Machine learning could become an increasingly important aspect of space exploration, from finding novel patterns in the science data transmitted from robotic spacecraft, to the operation of sustainable habitats. The sustainable habitat subtopic calls for machine learning technology in order to substantially improve diagnostic and prognostic performance for integrated systems health management. In addition, STTR subtopics related to machine learning are very relevant to autonomous systems technologies.

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