Onboard science data processing: ST6 autonomous science experiment
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Engineering topics
Publications and source records attributed to Castano, R..
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The Autonomous Sciencecraft Constellation-Techsat 21 mission is a NASA New Millennium Program mission selected for flight in 2004. ASC will autonomously perform science analyses of X-band radar data, while in Earth orbit. Additional information is contained in the original extended abstract.
We have developed an automated technique to allow a rover to quantify the shape and other geologic characteristics of rocks from two-dimensional visible wavelength images and three-dimensional stereo range data. Additional information is contained in the original extended abstract.
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We introduce a general type of optimization algorithm which infers data models relating two different but intertwined types of information about each of a set of objects.
This paper talks about the techsat-21 autonomous sciencecraft constellation.
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The paper talks about the planning, scheduling, and execution framework used in ASC (Autonomous Sciencecraft Constellation).
In this paper we introduce a general framework for an image based autonomous rock detection process for Martian terrain. A rock detection algorithm, based on this framework, is described and demonstrated on examples of real Mars Rover data. An attempt is made to produce a system that is independent of parameters to ease on-board implementation for real time in-situ operation.
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data.
To maximize the return on future planetary missions, it will be critical that rovers have the capability to analyze information onboard and select and return data that is most likely to yield valuable scientific discoveries. Additional information is contained in the original extended abstract.
The science return from future robotic exploration of the Martian surface can be enhanced by performing routine processing using on board computers.
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We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several parent clusters.
We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays.
The problem addressed in this paper is that clustering image pixels into regions of homogenous geological texture.
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data.