Abstract
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 language | English (US) |
|---|---|
| Title of host publication | Astrodynamics 2015 |
| Publisher | Univelt Inc. |
| Pages | 1959-1978 |
| Number of pages | 20 |
| Volume | 156 |
| ISBN (Print) | 9780877036296 |
| State | Published - 2016 |
| Event | AAS/AIAA Astrodynamics Specialist Conference, ASC 2015 - Vail, United States Duration: Aug 9 2015 → Aug 13 2015 |
Other
| Other | AAS/AIAA Astrodynamics Specialist Conference, ASC 2015 |
|---|---|
| Country | United States |
| City | Vail |
| Period | 8/9/15 → 8/13/15 |
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ASJC Scopus subject areas
- Aerospace Engineering
- Space and Planetary Science
Cite this
Relative optical navigation around small bodies via extreme learning machines. / Furfaro, Roberto; Law, Andrew M.
Astrodynamics 2015. Vol. 156 Univelt Inc., 2016. p. 1959-1978.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Relative optical navigation around small bodies via extreme learning machines
AU - Furfaro, Roberto
AU - Law, Andrew M.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85007336458&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85007336458
SN - 9780877036296
VL - 156
SP - 1959
EP - 1978
BT - Astrodynamics 2015
PB - Univelt Inc.
ER -