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Robotic Recon for Human ExplorationRobotic reconnaissance has the potential to significantly improve scientific and technical return from lunar surface exploration. In particular, robotic recon may increase crew productivity and reduce operational risk for exploration. However, additional research, development and field-testing is needed to mature robot and ground control systems, refine operational protocols, and specify detailed requirements. When the new lunar surface campaign begins around 2020, and before permanent outposts are established, humans will initially be on the Moon less than 10% of the time. During the 90% of time between crew visits, robots will be available to perform surface operations under ground control. Understanding how robotic systems can best address surface science needs, therefore, becomes a central issue Prior to surface missions, lunar orbiters (LRO, Kaguya, Chandrayyan-1, etc.) will map the Moon. These orbital missions will provide numerous types of maps: visible photography, topographic, mineralogical and geochemical distributions, etc. However, remote sensing data will not be of sufficient resolution, lighting, nor view angle, to fully optimize pre-human exploration planning, e.g., crew traverses for field geology and geophysics. Thus, it is important to acquire supplemental and complementary surface data. Robotic recon can obtain such data, using robot-mounted instruments to scout the surface and subsurface at resolutions and at viewpoints not achievable from orbit. This data can then be used to select locations for detailed field activity and prioritize targets to improve crew productivity. Surface data can also help identify and assess terrain hazards, and evaluate alternate routes to reduce operational risk. Robotic recon could be done months in advance, or be part of a continuing planning process during human missions.
Document ID
20090036808
Acquisition Source
Ames Research Center
Document Type
Other
Authors
Deans, Matthew
(NASA Ames Research Center Moffett Field, CA, United States)
Fong, Terry
(NASA Ames Research Center Moffett Field, CA, United States)
Ford, Ken
(Florida Univ. United States)
Heldmann, Jennifer
(NASA Headquarters Washington, DC United States)
Helper, Mark
(Texas Univ. Austin, TX, United States)
Hodges, Kip
(Arizona State Univ. United States)
Landis, Rob
(NASA Johnson Space Center Houston, TX, United States)
Lee, Pascal
(NASA Ames Research Center Moffett Field, CA, United States)
Schaber, Gerald
(Geological Survey United States)
Schmitt, Harrison H.
(Florida Univ. United States)
Date Acquired
August 24, 2013
Publication Date
January 9, 2009
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
ARC-E-DAA-TN622
Report Number: ARC-E-DAA-TN622
Funding Number(s)
WBS: WBS 431731.04.03.02
Distribution Limits
Public
Copyright
Public Use Permitted.
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