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A Robust Compositional Architecture for Autonomous SystemsSpace exploration applications can benefit greatly from autonomous systems. Great distances, limited communications and high costs make direct operations impossible while mandating operations reliability and efficiency beyond what traditional commanding can provide. Autonomous systems can improve reliability and enhance spacecraft capability significantly. However, there is reluctance to utilizing autonomous systems. In part this is due to general hesitation about new technologies, but a more tangible concern is that of reliability of predictability of autonomous software. In this paper, we describe ongoing work aimed at increasing robustness and predictability of autonomous software, with the ultimate goal of building trust in such systems. The work combines state-of-the-art technologies and capabilities in autonomous systems with advanced validation and synthesis techniques. The focus of this paper is on the autonomous system architecture that has been defined, and on how it enables the application of validation techniques for resulting autonomous systems.
Document ID
20060015098
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Brat, Guillaume
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Deney, Ewen
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Farrell, Kimberley
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Giannakopoulos, Dimitra
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Jonsson, Ari
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Frank, Jeremy
(NASA Ames Research Center Moffett Field, CA, United States)
Bobby, Mark
(Adventium Labs. Minneapolis, MN, United States)
Carpenter, Todd
(Adventium Labs. Minneapolis, MN, United States)
Estlin, Tara
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2006
Publication Information
ISBN: 0-7803-9546
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
ISBN: 0-7803-9546
Distribution Limits
Public
Copyright
Public Use Permitted.
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