Agile Science
Description
This project seeks to enable agile science to be conducted by remote, autonomous spacecraft beyond range of low-latency human control.
Detailed example
choose scientific targets of opportunity, conduct on-board prioritization, conduct geometric reasoning, and implement planning, scheduling, and execution
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Spacecraft systems will have to be able to conduct onboard analysis of sensor data & images to choose scientific targets of opportunity, conduct on-board prioritization, conduct geometric reasoning, and implement planning, scheduling, and execution. Future missions to primitive bodies and deep space exploration may have limited time to explore unknown targets and to react/adapt to new science opportunities.
Controls / human review
ATO: Not reported; PIA: Not published