Abstract

Non-Robotic Science Autonomy Development

Highlights

  • While data processing and analysis by experts is necessary for scientific advancement, science return would be enhanced by automating certain tasks

  • In addition to the well-known mechanical and operational challenges, there are constraints posed by data collection and transmission

  • There are methods that go beyond the classical compression and data partitioning schemes; novel machine learning methods can form the basis of an onboard data budget by making informed decisions based on the situation

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Summary

Key Points

1) Future planetary missions, especially those to the outer solar system, face significant challenges to increase sampling, reduce uncertainties, and manage and transmit increasing data volumes with limited data link rates. The 2015 NASA Technology Roadmaps detail the need for advancing autonomy to support future missions ranging from Discovery to Flagship classes, and to planetary targets ranging from the Moon to Mars, Venus, and Europa. Instruments capable of autonomous data collection, both robotically and in terms of decision-making (what samples to analyze, when, for how long, and fidelity of transmitted data) would, for example, greatly enhance the science return for missions in extreme environments, and are being planned for e.g., the proposed Europa Lander mission [2]. While data processing and analysis by experts is necessary for scientific advancement (and discoveries continue for years beyond a prime mission), science return would be enhanced by automating certain tasks Both types of science autonomy are challenged and motivated by ever-increasing data volumes as instruments evolve. Data volume for mass spectrometers is growing by orders of magnitude over

MSL ExoMars Future
Imaging System Narrow Angle
Findings
Recommendations for Advancing Science Autonomy
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