DEMO: autonomous science analysis, planning, and execution on the EO-1 mission
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EO-1 autonomous science ASE onboard decision-makin↗
Engineering topics
Burl, M.
Publications and source records attributed to Burl, M..
Autonomous Science on the EO-1 Mission
In mid-2003, we will fly software to detect science events that will drive autonomous scene selectionon board the New Millennium Earth Observing 1 (EO-1) spacecraft. This software will demonstrate the potential for future space missions to use onboard decision-making to detect science events and respond autonomously to capture short-lived science events and to downlink only the highest value science data.
autonomous systems robust execution↗
The Techsat-21 autonomous space science agent
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.
autonomy software agents onboard decision-making↗
Training of a crater detection algorithm for Mars crater imagery
This paper discusses the use of a crater detection algorithm for Mars crater imagery.
automatic feature identification↗
Observing active volcanism on Earth and beyond with an autonomous science investigation capability
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volcanism remote sensing autonomy new millenium pr↗
The Techsat-21 autonomous sciencecraft constellation
This paper talks about the techsat-21 autonomous sciencecraft constellation.
autonomous spacecraft constellation↗
A demonstration of robust planning, scheduling, and execution for the Techsat-21 autonomous sciencecraft constellation
The paper talks about the planning, scheduling, and execution framework used in ASC (Autonomous Sciencecraft Constellation).
autonomous spacecraft constellation ASC↗
The Techsat-21 Autonomous Sciencecraft Constellation demonstration
The Autonomous Sciencecraft Constellation flight demonstration (ASC) will fly onboard the Air Forces's TechSat-21 constellation. Demonstration of its capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreadable without this technology.
scheduling planning resources model based sytems↗
Automated Analysis of Radar Imagery of Venus: Handling Lack of Ground Truth
Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROC) for evaluating detection performance when the ground truth is subjective.
Venus Magellan remote sensing image analysis autom↗
A Statistical Approach to Adaptive Problem-Solving for Large-Scale Scheduling and Resource Allocation Problems
Our work focuses upon development of techniques for choosing among a set of alternatives in the presence of incomplete information and varying costs of acquiring information.
alternatives↗
On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach
This paper considers the decision-making problem of selecting a strategy from a set of alternatives on the basis of incomplete information (e.g., a finite number of observation). At any time the system can adopt a particular strategy or decide to gather additional information at some cost.
machine learning↗
On the Efficient Allocation of Resources for Hypothesis Evaluation in Machine Learning: A Statistical Approach
In this report we consider a decision-making problem of selecting a strategy from a set of alternatives on the basis of incomplete information (e.g., a finite number of observations): the system can, however, gather additional information at some cost.
machine learning↗