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Machine Learning for Slow but Steady Interplanetary ConstructionFor prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.
Document ID
20170011618
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Agogino, Adrian
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
December 8, 2017
Publication Date
August 29, 2017
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Mechanical Engineering
Report/Patent Number
ARC-E-DAA-TN46391
Meeting Information
Meeting: Machine Learning Workshop 2017
Location: Moffett Field, CA
Country: United States
Start Date: August 29, 2017
End Date: August 31, 2017
Sponsors: NASA Ames Research Center
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Keywords
Tensegrity robots
Multiagent systems
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