System for Uncertainty, Risk, and Feature Assessment of Surfaces (SURFAS) Holistic Scene Analysis

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

Translation and transcription Derived by keyword rules; see methodology

Problem the AI is intended to solve

SURFAS, given the above combination of inputs for a particular vehicle, is informed by expertise of mission designers to fuse relevant data together in order to create a holistic map of the scene, combining all information that the agent can perceive about that scene; e.g. the hyperspectral imager may autonomously tag identify an area of geological interest to the mission, Using data from a LiDAR scan, the robot can plan a path to reach the area. That path can be modified to avoid a patch of soil that the ground penetrating radar identifies as soft and loose (and so should be avoided by the robot). SURFAS intends to provide an architecture for uniting these telemetry streams into a single scene map that is made available to onboard planners to account for both risk AND reward in planning.

Expected benefits

SURFAS intends to provide an architecture for unifying onboard systems that are often siloed with limited data sharing. Particularly, science instruments can provide relevant traversal information, especially in novel environments encountered on planetary surfaces. We want to avoid leaving data on the table and instead use all available telemetry to inform the motion and mission planning for next-generation robotic explorers.

System outputs

Map “layers” of the scene by individual features (e.g. a layer containing only LiDAR and one containing only GPR), as well as a fused map, where each individual layer measurement is translated to a measure of mission risk versus reward, and combined with measurements from other layers. The final data product is analogous to a costmap used in terrestrial robotic planning, but encompassing all available aspects of the scene.

Technology

AI classification

Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.

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.

Potential impacts and how they were identified

Not reported