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Application of Machine-Learning Algorithms for On-Board Asteroid Shape Model DeterminationThe Application of Machine-learning Algorithms for On-board Asteroid Shape Model Determination project will develop an innovative system for spacecraft navigation to expand the capability of small spacecraft to meet the critical challenges associated with small-body exploration. Such challenges include accurate navigation in a microgravity environment and precision targeting of particular locations on an asteroid surface for sample collection. This on-board system will cut the computational "umbilical" back to Earth-currently necessary for the generation of a global shape model that requires thousands of images with sufficient resolution and adequate variation of incidence and emission angles, processed manually by a team of experts on Earth for several months. Small satellites have limited bandwidth and are unable to downlink the data volume required for this processing, restricting their ability to perform deep-space asteroid exploration.
Document ID
20180006633
Acquisition Source
Ames Research Center
Document Type
Other - Brief Communication/Note
Authors
Lauretta, Dante S.
(Arizona Univ. Tucson, AZ, United States)
Date Acquired
October 24, 2018
Publication Date
May 7, 2018
Subject Category
Computer Programming And Software
Report/Patent Number
ARC-E-DAA-TN55818
Fact Sheet 2018-03-05-ARC
Report Number: ARC-E-DAA-TN55818
Report Number: Fact Sheet 2018-03-05-ARC
Meeting Information
Meeting: Interplanetary Small Satellite Conference
Location: Pasadena, CA
Country: United States
Start Date: May 7, 2018
End Date: May 8, 2018
Sponsors: Jet Propulsion Lab., California Inst. of Tech.
Funding Number(s)
CONTRACT_GRANT: NNM10AA11C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Machine learning algorithms
small spacecraft
global shape modeling
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