The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A survey on artificial intelligence trends in spacecraft guidance dynamics and control
Open accessAbstract
The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of spacecraft guidance dynamics and control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.
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Cites
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Cited by
Target tracking control of UAV through deep reinforcement learning
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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
“[6] surveyed the recent developments in the area of spacecraft guidance dynamics and control, with a focus on evolutionary optimization, tree searches and ML (including DL and RL) as the principal drivers and technologies for current and future research in the field.”
BackgroundDeep Learning Crater Detection for Lunar Terrain Relative Navigation
Downes, L., Steiner, T. J., & How, J. P. (2020) · AIAA Scitech 2020 Forum · doi:10.2514/6.2020-1838 · Cited by 57
Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
Downes, L. M., Steiner, T. J., & How, J. P. (2020) · 2020 American Control Conference (ACC) · doi:10.23919/acc45564.2020.9147595 · Cited by 35
Metadata
- Authors
- Izzo, D., Märtens, M., & Pan, B.
- Venue
- Astrodynamics
- Published
- 2019-07-31
- Volume
- 3
- Issue
- 4
- Pages
- 287-299
- ISSN
- 2522-008X (ISSN-L)
- 2522-0098
- OpenAlex
- W2965712423
- Cited by
- 281
- References
- 48
- FWCI
- 23.61
- Type
- journal article
- Language
- en
- Open access
- green
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