Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization |
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Authors: | Roberto Furfaro Riccardo Barocco Richard Linares Francesco Topputo Vishnu Reddy Jules Simo Lucille Le Corre |
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Institution: | 1. Department of Systems and Industrial Engineering, Department of Aerospace and Mechanical Engineering, University of Arizona, 1127 E. James E. Roger Way, Tucson, AZ 85721, USA;2. Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 86721, USA;3. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;4. Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano, viaLa Masa 34, 20156 Milano, Italy;5. Department of Planetary Science, Lunar and Planetary Laboratory, University of Arizona, 1127 E. James E. Roger Way, Tucson, AZ 85721, USA;6. School of Engineering, University of Central Lancashire, Preston, Lancashire PR1 2HE, UK;7. Planetary Science Institute, 1700 East Fort Lowell, Suite 106, Tucson, AZ 85719, USA |
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Abstract: | Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface. |
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Keywords: | Extreme learning machine Gravity modeling Asteroid |
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