2 research outputs found
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Machine learning (ML) methods can expand our ability to construct, and draw
insight from large datasets. Despite the increasing volume of planetary
observations, our field has seen few applications of ML in comparison to other
sciences. To support these methods, we propose ten recommendations for
bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for
decadal survey), 3 figures, white paper submitted to the Planetary Science
and Astrobiology Decadal Survey 2023-203
Advancing the Scientific Frontier with Increasingly Autonomous Systems
A close partnership between people and partially autonomous machines has
enabled decades of space exploration. But to further expand our horizons, our
systems must become more capable. Increasing the nature and degree of autonomy
- allowing our systems to make and act on their own decisions as directed by
mission teams - enables new science capabilities and enhances science return.
The 2011 Planetary Science Decadal Survey (PSDS) and on-going pre-Decadal
mission studies have identified increased autonomy as a core technology
required for future missions. However, even as scientific discovery has
necessitated the development of autonomous systems and past flight
demonstrations have been successful, institutional barriers have limited its
maturation and infusion on existing planetary missions. Consequently, the
authors and endorsers of this paper recommend that new programmatic pathways be
developed to infuse autonomy, infrastructure for support autonomous systems be
invested in, new practices be adopted, and the cost-saving value of autonomy
for operations be studied.Comment: 10 pages (compared to 8 submitted to PSADS), 2 figures, submitted to
National Academy of Sciences Planetary Science and Astrobiology Decadal
Survey 2023-203