Andrea Scorsoglio

Andrea Scorsoglio has earned his bachelor's and master's degrees at Politecnico di Milano in aerospace engineering, and his Ph.D. in systems engineering at the University of Arizona. He is now a postdoctoral research associate at the Space Systems Engineering Laboratory at the University of Arizona. His research focuses on applications of reinforcement learning and physics informed neural networks to spacecraft guidance navigation and control and orbit determination. He is also part of the target follow-up working group within the science team of the NASA mission named Near Earth Object Surveillance Mission, working on a decision support tool for target follow-up.

Degrees

• MSc, Space Engineering, Politecnico di Milano (2018)
• BSc, Aerospace Engineering, Politecnico di Milano (2016)

Publications

• Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2020). Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. arXiv preprint arXiv:2004.09978. PDF
• Scorsoglio, A., & Furfaro, R. (2019). ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbit. In 2019 AAS/AIAA Astrodynamics Specialists Conference (pp. 1-20). PDF
• Furfaro, R., Scorsoglio, A., Linares, R., & Massari, M. (2020). Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronautica. DOI: http://dx.doi.org/10.1016/j.actaastro.2020.02.051
• Holt, H., Armellin, R., Scorsoglio, A., & Furfaro, R. (2020). Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters. In AIAA Scitech 2020 Forum (p. 1694). DOI: https://doi.org/10.2514/6.2020-1694
• Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B. (2020). Image-based Deep Reinforcement Learning for Autonomous Lunar Landing. In AIAA Scitech 2020 Forum (p. 1910). DOI: http://dx.doi.org/10.2514/6.2020-1910
• Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2019). Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. In 29th AAS/AIAA Space Flight Mechanics Meeting (pp. 1-20).  PDF
• Scorsoglio, A., & Furfaro, R. (2019), ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbits. Conference: 2019 AAS/AIAA Astrodynamics Specialist Conference. Portland, ME, USA. PDF

Conference Presentations

• 30th AIAA/AAS Space Flight Mechanics Meeting/AIAA SciTech Forum and Exposition, Orlando, Florida (USA), 6-10 January 2020, Image-based Deep Reinforcement Learning for Autonomous Landing.
• AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine (USA), 11-15 August 2019. ELM-based Actor-Critic approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbits.
• 29th AIAA/AAS Space Flight Mechanics Meeting, Ka'anapali, Hawaii (USA), 13-17 January 2019. Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit.