Dr. Stefano Silvestrini
Politecnico di Milano, Milan, Lombardy, Italy
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Academic Career Length
29Years (1998-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2020 in Aerospace Science and Technology
Journal Article
VPVincenzo Pesce
Vincenzo PesceStefano SilvestriniMLMichèle Lavagna
Michèle Lavagna
Journal Article
Stefano SilvestriniMLMichèle Lavagna
Michèle LavagnaThe growing interest in Artificial Intelligence is pervading several domains of technology and robotics research. Only recently has the space community started to investigate deep learning methods and artificial neural networks for space systems. This paper aims at introducing the most relevant characteristics of these topics for spacecraft dynamics control, guidance and navigation. The most common artificial neural network architectures and the associated training methods are examined, trying to highlight the advantages and disadvantages of their employment for specific problems. In particular, the applications of artificial neural networks to system identification, control synthesis and optical navigation are reviewed and compared using quantitative and qualitative metrics. This overview presents the end-to-end deep learning frameworks for spacecraft guidance, navigation and control together with the hybrid methods in which the neural techniques are coupled with traditional algorithms to enhance their performance levels.
18 March 2022 in Aerospace Science and Technology
Journal Article
Stefano SilvestriniMPMargherita Piccinin
Margherita PiccininGZGiovanni Zanotti
Giovanni ZanottiABAndrea Brandonisio
Andrea Brandonisio
IBIlaria Bloise
Ilaria Bloise
MVMattia Varile
Mattia Varile
+3
1 August 2019 in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Journal Article
FFF. Fuschino
F. Fuschino
RCRiccardo Campana
Riccardo CampanaCLC. Labanti
C. LabantiYEY. Evangelista
Y. EvangelistaMFM. Feroci
M. FerociNZNicola Zorzi
Nicola Zorzi +38
1 December 2021 in Journal of Guidance, Control, and Dynamics
Journal Article
Stefano SilvestriniMLMichèle Lavagna
Michèle Lavagna
Journal Article
Matteo D'AmbrosioLCLorenzo Capra
Lorenzo CapraABAndrea Brandonisio
Andrea BrandonisioStefano SilvestriniMLMichèle Lavagna
Michèle LavagnaThe application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission.
About Me
Dr. Stefano Silvestrini is a post-doctoral researcher at the Aerospace Science and Technology Department of Politecnico di Milano. He obtained his Ph.D. cum laude with a thesis titled "AI-augmented Guidance, Navigation and Control for Proximity Operations of Distributed Systems." He earned his M.Sc. degree in Aerospace Engineering in 2017 from TU Delft. In 2016, he has worked as a trainee for Airbus D&S in Munich. In 2015, he earned his B.Sc. degree in Aerospace Engineering at the Università degli Studi di Padova. During his BSc, he spent a 6-month research period at the College of Aerospace Engineering of Boston University under a scholarship. He has been involved in national and EU/ESA-funded projects for developing a nanosat constellation for science observation, mission analysis and system design for fractionated space architecture and AI for spacecraft GNC. He has worked as a teaching assistant for several courses throughout his MSc and PhD career. His research interests include the development of artificial intelligence algorithms for autonomous GNC in distributed space systems and proximity operations, particularly tailored for embedded applications in small platforms.