NASA · NASA-234 · record R0170
MLNav (Machine Learning Navigation)
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
- Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact
- Impact justification
- Low-speed engagements with terrain features on Mars; part of core mission parameters. Mars Rover cannot harm humans or impact rights.
- Start date
- 2020-07-01 (day precision)Source: 07/01/2020 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
Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Expected benefits
Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
System outputs
Path planning recommendations for Mars2020 Rover
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
- Real terrain data from Mars on ENav simulator
- 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