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Machine Learning an...
Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design
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Izzo, D. (författare)
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- Sprague, Christopher (författare)
- KTH,Robotik, perception och lärande, RPL
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Tailor, D. V. (författare)
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(creator_code:org_t)
- 2019-05-11
- 2019
- Engelska.
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Ingår i: Modeling and Optimization in Space Engineering. - Cham : Springer International Publishing. ; , s. 191-210
- Relaterad länk:
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http://arxiv.org/pdf...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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visa färre...
Abstract
Ämnesord
Stäng
- After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth–Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kap (ämneskategori)
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