Machine learning in space: extending our reach
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Using the Autonomous Sciencecraft Experiment to automatically plan and adaptively re-plan observations, the EO-1 spacecraft cut operational costs by $1M per year with a 50% increase in science return.
“For example, by using the Autonomous Sciencecraft Experiment to automatically plan and adaptively re-plan observations as required the EO-1 spacecraft was able to cut operational costs by $1 M per year, with a 50% increase in science return [118].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [118]
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Cites
Using Autonomy Flight Software to Improve Science Return on Earth Observing One
Chien, S., Sherwood, R., Tran, D. Q., et al. (2005) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.12923 · Cited by 243 · see citing works
Autonomy for Mars Rovers: Past, Present, and Future
Bajracharya, M., Maimone, M. W., & Helmick, D. M. (2008) · Computer · doi:10.1109/mc.2008.479 · Cited by 200 · see citing works
Autonomous Navigation Results from the Mars Exploration Rover (MER) Mission
Maimone, M., Johnson, A., Cheng, Y., Willson, R., & Matthies, L. (2006) · Springer Tracts in Advanced Robotics · doi:10.1007/11552246_1 · Cited by 87 · see citing works
Automatic detection of dust devils and clouds on Mars
Castaño, A., Fukunaga, A. S., Biesiadecki, J. J., et al. (2007) · Machine Vision and Applications · doi:10.1007/s00138-007-0081-3 · Cited by 83 · see citing works
Building an autonomous humanoid tool user
Bluethmann, W., Ambrose, R. O., Diftler, M. A., et al. (2005) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2004.1442134 · Cited by 26 · see citing works
On-board analysis of uncalibrated data for a spacecraft at mars
Castaño, R., Wagstaff, K. L., Chien, S., et al. (2007) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281291 · Cited by 25 · see citing works
Self-citationLearning classifiers for science event detection in remote sensing imagery
Castaño, R. (2005) · International Symposium on Artificial Intelligence · Cited by 21 · see citing works
Mission Operations of Earth Observing-1 with Onboard Autonomy
Gregg Rabideau, D. T. (2006) · 2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06) · doi:10.1109/smc-it.2006.48 · Cited by 21 · see citing works
Making Smarter, Savvier Robots
Kean, S. (2010) · Science · doi:10.1126/science.329.5991.508 · Cited by 19 · see citing works
Resilient k-d trees: k-means in space revisited
Gieseke, F. C., Moruz, G., & Vahrenhold, J. (2012) · Frontiers of Computer Science · doi:10.1007/s11704-012-2870-8 · Cited by 12 · see citing works
Automated Targeting for the MER Rovers
Estlin, T. A., Castaño, R., Bornstein, B., et al. (2009) · IEEE International Conference on Space Mission Challenges for Information Technology · doi:10.1109/smc-it.2009.38 · Cited by 12 · see citing works
Wagstaff, K. L., & Bornstein, B. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553514 · Cited by 11 · see citing works
Self-citationHow much Memory Radiation Protection do Onboard Machine Learning Algorithms Require
Wagstaff, K. L., & Bornstein, B. (2009) · Cited by 5 · see citing works
Self-citationResilient K-d Trees: K-Means in Space Revisited
Gieseke, F., Moruz, G., & Vahrenhold, J. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.94 · Cited by 3 · see citing works
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Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“It also found out that simpler algorithms (regular k-means clustering and linear SVM) have less sensitivity (more tolerance) than more complex versions (kd-k-means, Gaussian SVM [117, 233].”
BackgroundInfluentialOnline continual streaming learning for embedded space applications
Mazouz, A. E., & Nguyen, V.-T. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01438-4 · Cited by 3 · see citing works
- Authors
- McGovern, A., & Wagstaff, K. L.
- Affiliations
- University of Oklahoma (US)
- California Institute of Technology (US)
- Corresponding author
- Wagstaff, Kiri L.
- Venue
- Machine Learning
- Published
- 2011-04-30
- Volume
- 84
- Issue
- 3
- Pages
- 335-340
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2155246336
- Cited by
- 36
- References
- 14
- Citations by year
- 2026: 1 · 2024: 6 · 2023: 4 · 2022: 4 · 2021: 8 · 2020: 5 · 2019: 4 · 2017: 2 · 2016: 2
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- California Institute of Technology
- University of Oklahoma
- Jet Propulsion Laboratory
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Space Technology and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Space exploration and regulation
- Keywords
- space missions, autonomous spacecraft, space robotics, mission reliability, science return
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