Timothy M. Stough

The Techsat-21 autonomous space science agent

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat-21 constellation of three spacecraft scheduled for launch in 2004. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. We also describe our working software prototype and preparations for flight.

On-board analysis of uncalibrated data for a spacecraft at mars

Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen. In this paper, we describe how we addressed the unique challenges associated with on-board mining of data as it is collected: uncalibrated data, noisy observations, and severe limitations on computational and memory resources. The goal of this effort, which falls into the emerging application area of spacecraft-based data mining, was to study three specific science phenomena on Mars. Following previous work that used a linear support vector machine (SVM) on-board the Earth Observing 1 (EO-1)spacecraft, we developed three data mining techniques for use on-board the Mars Odyssey spacecraft. These methods range from simple thresholding to state-of-the-art reduced-set SVM technology. We tested these algorithms on archived data in a flight software testbed. We also describe a significant, serendipitous science discovery of this data mining effort: the confirmation of a water ice annulus around the north polar cap of Mars. We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft.

E-DECIDER: Using Earth Science Data and Modeling Tools to Develop Decision Support for Earthquake Disaster Response

Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) is a NASA-funded project developing new capabilities for decision making utilizing remote sensing data and modeling software to provide decision support for earthquake disaster management and response. E-DECIDER incorporates the earthquake forecasting methodology and geophysical modeling tools developed through NASA’s QuakeSim project. Remote sensing and geodetic data, in conjunction with modeling and forecasting tools allows us to provide both long-term planning information for disaster management decision makers as well as short-term information following earthquake events (i.e. identifying areas where the greatest deformation and damage has occurred and emergency services may need to be focused). This in turn is delivered through standards-compliant web services for desktop and hand-held devices.

Earth Science Datacasting: Informed Pull and Information Integration

The intent of Datacasting is to empower consumers of Earth science data with the ability to extract from a stream of data granules (or files) precisely those granules that are required to meet a predefined need, for example, ldquoAcquire from a MODIS L2 data stream only the granules that contain information about a wild fire in Southern California.rdquo Our approach to solving this problem has been to take the concept of Really Simple Syndication (RSS) feeds, for delivering regularly changing web content, and extend this to represent a stream of data granules and deliver regularly changing Earth science data content. In essence, this project is doing for Earth science what Podcasting has done for audio and video. Where Podcasting extended RSS to revolutionize how users access audio and video content provided by various media outlets, so Datacasting extends RSS to provide users with the ability to download data granules provided by Earth science data providers as the data are made available. Moreover, we have taken the concept one step further by creating a solution for filtering on the metadata of a feed in order to identify granules of interest based on user-defined criteria. In this paper, we also show how Datacasting feeds can be combined with other RSS-based feeds to identify relationships between information sources and extract new knowledge, as well as aid the development of new geo-based web services not currently envisaged.