AEGIS: Autonomous Exploration for Gathering Increased Science
National Aeronautics and Space Administration · JPL: Jet Propulsion Laboratory
Overview
- Development stage
- DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- 2010-01-01 (day precision)Source: 01/01/2010 00:00:00
- 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
AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses computer vision techniques to identify targets (e.g., rocks) in wide angles images of the rover's surrounding terrain. If targets are found that match scientists specificiations, they are then measured autonomously using remote sensing instruments. AEGIS was first used on the MER Mission. It is currently in use on the MSL Mission to acquire data for the ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to acquire data for the SuperCam instrument.
Expected benefits
AEGIS enables intelligent targeting and data acquisition by planetary rovers. It uses computer vision techniques to identify targets (e.g., rocks) in wide angles images of the rover's surrounding terrain. If targets are found that match scientists specificiations, they are then measured autonomously using remote sensing instruments. AEGIS was first used on the MER Mission. It is currently in use on the MSL Mission to acquire data for the ChemCam instrument. It is planned for use in Spring of 2022 on the M2020 Mission to acquire data for the SuperCam instrument.
System outputs
Recommendations of relevant objects, e.g., Mars rocks, for scientific examination.
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)
- Mars2020 Rover
- Custom-developed code
- Yes
- Public source code
- Not reported
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- 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
- No
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- No
- Demographic features
- Not reported
- Training and evaluation data
- wide angles images of the rover's surrounding terrain
- 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.
Potential impacts and how they were identified
Not reported