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Machine Learning for Slow but Steady Interplanetary Construction
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Author and Affiliation:
Agogino, Adrian(NASA Ames Research Center, Moffett Field, CA, United States)
Abstract: For 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.
Publication Date: Aug 29, 2017
Document ID:
20170011618
(Acquired Dec 14, 2017)
Subject Category: MECHANICAL ENGINEERING; CYBERNETICS, ARTIFICIAL INTELLIGENCE AND ROBOTICS
Report/Patent Number: ARC-E-DAA-TN46391
Document Type: Oral/Visual Presentation
Meeting Information: Machine Learning Workshop 2017; 29-31 Aug. 2017; Moffett Field, CA; United States
Meeting Sponsor: NASA Ames Research Center; Moffett Field, CA, United States
Financial Sponsor: NASA Ames Research Center; Moffett Field, CA, United States
Organization Source: NASA Ames Research Center; Moffett Field, CA, United States
Description: 18p; In English
Distribution Limits: Unclassified; Publicly available; Unlimited
Rights: No Copyright; Work of the U.S. Government - Public use permitted
NASA Terms: MACHINE LEARNING; CONSTRUCTION; ROBOTS; PLANETARY LANDING; COMPLEX SYSTEMS; COORDINATION; MANAGEMENT ANALYSIS; ROBOTICS; HABITATS; MANNED SPACE FLIGHT; RELIABILITY
Other Descriptors: MULTIAGENT SYSTEMS; TENSEGRITY ROBOTS
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