T. C. Doggett

The EO-1 Autonomous Science Agent

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe the constraints and how they were addressed in our agent design, validation, and deployment.

Onboard classifiers for science event detection on a remote sensing spacecraft

Typically, data collected by a spacecraft is downlinked to Earth and preprocessed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up.Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.The manual and SVM classifiers have been uploaded to the EO-1 spacecraft and have been running onboard the spacecraft for over a year. Results of the onboard analysis are used by the Autonomous Sciencecraft Experiment (ASE) of NASAs New Millennium Program onboard EO-1 to automatically target the spacecraft to collect follow-on imagery. The software demonstrates the potential for future deep space missions to use onboard decision making to capture short-lived science events.

An autonomous Earth observing sensorweb

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This sensor network is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of multiple science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the Earth observing sensorWeb application as well as overall architecture for the network

Onboard Autonomy on the Earth Observing One Mission

The Earth Observing One Spacecraft is currently flying The Autonomous Sciencecraft Experiment (ASE) - onboard autonomy software to improve science return. The ASE software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations.

An autonomous earth observing sensorWeb

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This sensor network is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of multiple science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the Earth observing sensorWeb application as well as overall architecture for the network.

Onboard classification of hyperspectral data on the Earth Observing One mission

Remote-sensed hyperspectral data represents significant challenges in downlink due to its large data volumes. This paper describes a research program designed to process hyperspectral data products onboard spacecraft to (a) reduce data downlink volumes and (b) decrease latency to provide key data products (often by enabling use of lower data rate communications systems). We describe efforts to develop onboard processing to study volcanoes, floods, and cryosphere, using the Hyperion hyperspectral imager and onboard processing for the Earth Observing One (EO-1) mission as well as preliminary work targeting the Hyperspectral Infrared Imager (HyspIRI) mission.