Relative Optical Navigation around Small Bodies via Extreme Learning Machines

Persistent Link:
http://hdl.handle.net/10150/593622
Title:
Relative Optical Navigation around Small Bodies via Extreme Learning Machines
Author:
Law, Andrew M.
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.
Type:
text; Electronic Thesis
Keywords:
Extreme Learning Machine; Navigation; Neural Network; Relative Optical Navigation; Small Bodies; Aerospace Engineering; Artificial Intelligence
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Aerospace Engineering
Degree Grantor:
University of Arizona
Advisor:
Furfaro, Roberto

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleRelative Optical Navigation around Small Bodies via Extreme Learning Machinesen_US
dc.creatorLaw, Andrew M.en
dc.contributor.authorLaw, Andrew M.en
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractTo perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.en
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectExtreme Learning Machineen
dc.subjectNavigationen
dc.subjectNeural Networken
dc.subjectRelative Optical Navigationen
dc.subjectSmall Bodiesen
dc.subjectAerospace Engineeringen
dc.subjectArtificial Intelligenceen
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorFurfaro, Robertoen
dc.contributor.committeememberFurfaro, Robertoen
dc.contributor.committeememberButcher, Ericen
dc.contributor.committeememberGaylor, Daviden
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