Toward Resilient Spacecraft: Artificially Intelligent Reasoning for Diagnosing Safe Modes
This research will develop a proof-of-concept prototype for an intelligent and explainable reasoning system on-board, which is capable of diagnosis and decision making in real-tim…
Towards Scientific Autonomy: Applying Machine Learning to MOMA Science Data
This work progresses NASA toward Scientific Autonomy by Applying Machine Learning to MOMA (Mars Organic Molecule Analyzer) Science Data to help search for signs of life.
Training Data for Streamflow Estimation
This work uses convolutional neural networks to refine training data for streamflow estimation. The project will implement, test, and operationalize a system to derive effective s…
Unification of laboratory and observational data via learning algorithms for robust models of ice microphysics
This work applies neural networks and variational autoencoders to a variety of weather analysis
Machine Learning Airport Surface Model: Estimated ON Time Prediction
The ML-airport-estimated-ON software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models inten…
Using Machine Learning to Detect and Build Calibrated CME Datasets
This work uses "you only look once" v.3 Machine Learning techniques to detect and build calibrated CME (coronal mass ejection) datasets based on other solar sensor data.
Vlasov Informed Super Resolution (VISR)
This work uses a physics informed neural network to gain insight into the Vlasov equation (key to plasma physics) based on plasma data from the Magnetospheric Multiscale mission s…
AEGIS: Autonomous Exploration for Gathering Increased Science
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 rov…
Agile Science
This project seeks to enable agile science to be conducted by remote, autonomous spacecraft beyond range of low-latency human control.
ASPEN Mission Planner
Based on AI techniques, ASPEN is a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. ASPEN provi…
Machine Learning Airport Surface Model: Taxi-in Prediction
The ML-airport-taxi-in software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended f…
CLASP Coverage Planning & Scheduling
The Compressed Large-scale Activity Scheduling and Planning (CLASP) project is a long-range scheduler for space-based or aerial instruments that can be modeled as pushbrooms 1D li…
Dynamic Targeting
In this approach, called Dynamic Targeting (DT), traditional broad swath instruments are supplemented by more focused instruments with narrow swath and/or limited duty cycle. Thes…
Enhanced AutoNav for Perseverance Rover on Mars
AutoNav on the Perseverance Rover autonomously plans a safe path based on stereo navigation camera images, based on multiple technologies including a tree search for decision maki…
Groundwater data interpolation in California’s Central Valley using multimodal data fusion and multivariate sequence-to-sequence transformation models
We describe novel distributed Artificial Intelligence/Multi Agent algorithms to allocate observations in a constellation and compare their performance to centralized and highly di…
Hybrid On-Board and Ground-Based Processing of Massive Sensor Data (HyspIRI IPM)
Future space missions will enable unprecedented monitoring of the Earth's environment and will generate immense volumes of science data. Getting this data to ground communications…
Machine Learning Airport Surface Model: Taxi-out Prediction
The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended…
Mars2020 Rover (Perseverance)
Research, experiments, and engineering to empower future rovers with onboard autonomy; planning, scheduling & execution; path planning; onboard science; image processing; terrain…
MLNav (Machine Learning Navigation)
Accelerates path planning of rovers and other types of vehicles through ML-based heuristics, while guaranteeing safety through conventional, model-based collision checking.
Neural network accelerated radiative transfer modeling
Neural network accelerated radiative transfer modeling is intended to enhance efforts in the Earth Science domain.
Perseverance Rover on Mars - Terrain Relative Navigation
3D machine vision via dual cameras to inform convolutional neural networks for rover navigation path planning.
Providing visualization tools and streamlining the detection and tracking of wildfire-induced smoke plumes during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) mission
Providing visualization tools and streamlining the detection and tracking of wildfire-induced smoke plumes during the Fire Influence on Regional to Global Environments and Air Qua…
SCOTI (Scientific Captioning of Terrain Images)
SCOTI (Scientific Captioning of Terrain Images) automatically generates natural language explanations of geological images taken by rovers. It uses "show-attend-tell" model consis…
SPOC (Soil Property and Object Classification)
Using a convolutional neural network (CNN), SPOC (Soil Property and Object Classification) takes rover images and classifies the terrain type (e.g., sand, soil) from visual appear…