20 results found
Amato D, McMahon JW, 2021, Deep learning method for Martian atmosphere reconstruction, Journal of Aerospace Information Systems, Vol: 18, Pages: 1-1, ISSN: 2327-3097
The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of postflight mission analysis. This paper proposes a deep learning architecture using a long short-term memory (LSTM) network for the reconstruction of Martian density and wind profiles from inertial measurements and guidance commands. The LSTM is trained on a large set of Mars entry trajectories controlled through the fully numerical predictor-corrector entry guidance (FNPEG) algorithm, with density and wind from the Mars Global Reference Atmospheric Model (GRAM) 2010. The training of the network is examined, ensuring that the LSTM generalizes well to samples not present in the training set, and the performance of the network is assessed on a separate training set. The errors of the reconstructed density and wind profiles are, respectively, within 0.54 and 1.9%. Larger wind errors take place at high altitudes due to the decreased sensitivity of the trajectory in regions of low dynamic pressure. The LSTM architecture reliably reproduces the atmospheric density and wind encountered during descent.
Bombardelli C, Falco G, Amato D, et al., 2021, Space occupancy in low-earth orbit, Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control, Vol: 44, Pages: 684-700, ISSN: 0731-5090
With the upcoming launch of large constellations of satellites in the low-Earth orbit (LEO) region, it will become important to organize the physical space occupied by the different operating satellites in order to minimize critical conjunctions and avoid collisions. This paper introduces the definition of space occupancy as the domain occupied by an individual satellite as it moves along its nominal orbit under the effects of environmental perturbations throughout a given interval of time. After showing that space occupancy for the zonal problem is intimately linked to the concept of frozen orbits and proper eccentricity, frozen orbit initial conditions are provided in osculating element space and a frozen-orbit polar equation is obtained to describe the space occupancy region in closed analytical form. Next, the problem of minimizing space occupancy is analyzed in a realistic model including tesseral harmonics, third-body perturbations, solar radiation pressure, and drag. The corresponding initial conditions, leading to minimum space occupancy (MiSO) orbits, are obtained numerically for a set of representative configurations in LEO. The implications for the use of MiSO orbits to optimize the design of mega-constellations are discussed.
Fuentes-Muñoz O, Pedros-Faura A, Amato D, 2021, Effect of non-Keplerian MOID evolution on preliminary keyhole analyses, 7th IAA Planetary Defense Conference – PDC 2021
Amato D, Hume S, Roelke E, et al., 2020, Deep learning atmospheric prediction algorithm for enhanced Mars EDL guidance, International Symposium on Artificial Intelligence, Robotics, and Automation in Space
Uncertainty in atmospheric density and wind is a major cause of suboptimalperformance in the Entry, Descent, and Landing (EDL) guidance at Mars. We improve the robustness of current EDL guidance algorithms to uncertain dynamic environments by proposing a reliable on-board atmospheric estimation algorithm. The algorithm consists of a deep, recurrent neural network using an efficient architecture for time-series predictions, the Long Short-Term Memory (LSTM) cell. The LSTM network is trained on entry trajectories simulated with the Fully Numerical Predictor-corrector Guidance (FNPEG); in each trajectory the vehicle is subject to density and wind fields from instances of the Mars Global Reference Atmospheric Model (GRAM) 2010. Predictions of density and wind as a function of altitude expected along the trajectory are obtained from onboard acceleration measurements and state estimates. The algorithm achieves a RMS value over time for the relative density error in the order of 10 % for samples in the validation dataset, and significantly improves performance with respect to an exponential fit to the density.
Amato D, Hume S, Grace B, et al., 2020, Mars EDL and aerocapture guidance under dynamic uncertainty, AAS/AIAA Astrodynamics Specialist Conference
Amato D, Malhotra R, Sidorenko V, et al., 2020, Lunar close encounters compete with the circumterrestrial Lidov–Kozai effect, Celestial Mechanics and Dynamical Astronomy, Vol: 132, Pages: 35-35, ISSN: 1572-9478
Amato D, Hume S, Grace B, et al., 2020, Robustifying Mars descent guidance through neural networks, AAS Guidance, Navigation, and Control Conference
Amato D, Bombardelli C, Baù G, et al., 2019, Non-averaged regularized formulations as an alternative to semi-analytical orbit propagation methods, Celestial Mechanics and Dynamical Astronomy, Vol: 131, Pages: 21-21, ISSN: 1572-9478
Amato D, Bombardelli C, Dell’Elce L, et al., 2019, Recovering the chaotic orbit of Cosmos 862, Toulouse, France
Amato D, Furfaro R, Rosengren AJ, et al., 2018, Attitude propagation of Resident Space Objects with Recurrent Neural Networks, Maui, HI, United States
Amato D, Rosengren AJ, Bombardelli C, 2018, THALASSA: a fast orbit propagator for near-Earth and cislunar space, Kissimmee, Florida, Publisher: American Institute of Aeronautics and Astronautics
Amato D, Rosengren AJ, Baù G, 2018, What Happened to Luna-3? A Numerical Exploration of Cislunar Dynamics, College Station, Texas, USA
Rosengren AJ, Amato D, Bombardelli C, et al., 2018, Resident space object proper orbital elements, Maui, Hawaii, USA
Amato D, Baù G, Bombardelli C, 2017, Accurate orbit propagation in the presence of planetary close encounters, Monthly Notices of the Royal Astronomical Society, Vol: 470, Pages: 2079-2099, ISSN: 0035-8711
Abstract. We present an efficient strategy for the numerical propagation of small Solar system objects undergoing close encounters with massive bodies. The tra
Hernando-Ayuso J, Amato D, Bombardelli C, 2017, Last-minute semi-analytical asteroid deflection by nuclear explosion, Tokyo, Japan
Amato D, 2017, Advanced orbit propagation methods applied to asteroids and space debris
Amato D, Bombardelli C, Baù G, 2016, Efficient numerical propagation of planetary close encounters with regularized element methods, 6th International Conference on Astrodynamics Tools and Techniques (ICATT 2016)
Close encounters with major Solar System bodies may bring about a strong amplification of numerical error during inter-planetary orbit propagation. In this work, we reduce global numerical error by integrating regularized equations of motion instead of the classical Newtonian equations in Cartesian coordinates. The integration performance of several sets ofregularized equations is assessed from large-scale numeri-cal propagations of close encounters in the Sun-Earth planar CR3BP. An essential device consists in switching between primary bodies during the propagation, which effectively decomposes a strongly-perturbed heliocentric problem into two weakly-perturbed ones; this propagation approach has been dubbed Online Trajectory Matching (OTM). Through this simple expedient, regularized equations describing the evolution of non-classical orbital elements achieve excellent performances compared to Newtonian equations, even when employing sophisticated adaptive numerical schemes.Further improvements might be expected by carefully selecting the location of the switch of primary bodies during the propagation.
Amato D, Bombardelli C, 2016, Advanced Orbit Propagation Methods and Application to Space Debris Collision Avoidance, Asteroid and Space Debris Manipulation: Advances from the Stardust Research Network, Editors: Vasile, Minisci, Reston ,VA, Publisher: American Institute of Aeronautics and Astronautics, Inc.
Bombardelli C, Amato D, Cano JL, 2016, Mission analysis for the ion beam deflection of fictitious asteroid 2015 PDC, Acta Astronautica, Vol: 118, Pages: 296-307, ISSN: 0094-5765
Cano JL, Amato D, 2016, Orbital Dynamics About Small Bodies, Asteroid and Space Debris Manipulation: Advances from the Stardust Research Network, Editors: Vasile, Minisci, Reston ,VA, Publisher: American Institute of Aeronautics and Astronautics, Inc.
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