Relative optical navigation around small bodies via extreme learning machines

Roberto Furfaro, Andrew M. Law

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


To perform close proximity operations under a low-gravity environment, relative and absolute position are vital information to the spacecraft maneuver. Hence navigation is inseparably integrated in space travel. This paper presents Extreme Learning Machine (ELM) as an optical navigation method around small celestial bodies. ELM is a Single Layer feed-Forward Network (SLFN), a brand of neural network (NN). The algorithm based on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model composes of the output weights which can be used to develop into a hypotheses. The proposed method is used to estimate the position of the spacecraft from optical images obtained through a navigation camera. The results show this approach is promising and potentially suitable for on-board navigation.

Original languageEnglish (US)
Title of host publicationAstrodynamics 2015
EditorsJames D. Turner, Geoff G. Wawrzyniak, William Todd Cerven, Manoranjan Majji
PublisherUnivelt Inc.
Number of pages20
ISBN (Print)9780877036296
StatePublished - 2016
EventAAS/AIAA Astrodynamics Specialist Conference, ASC 2015 - Vail, United States
Duration: Aug 9 2015Aug 13 2015

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


OtherAAS/AIAA Astrodynamics Specialist Conference, ASC 2015
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science


Dive into the research topics of 'Relative optical navigation around small bodies via extreme learning machines'. Together they form a unique fingerprint.

Cite this