NASA · NASA-215 · record R0162

Agile Science

National Aeronautics and Space Administration · JPL: Jet Propulsion Laboratory

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

This project seeks to enable agile science to be conducted by remote, autonomous spacecraft beyond range of low-latency human control.

Expected benefits

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.

System outputs

choose scientific targets of opportunity, conduct on-board prioritization, conduct geometric reasoning, and implement planning, scheduling, and execution

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported