Andrea Scorsoglio
Papers
1
Total Citations
104
H-Index
1
About
Andrea Scorsoglio is a leading researcher in the field of autonomous space guidance and control, with a primary focus on deep reinforcement learning (DRL) for planetary landing and orbital maneuvers. Her most influential work, "Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach" (2020), has garnered over 104 citations, establishing her as a pioneer in integrating machine learning with classical guidance laws. Scorsoglio’s major contribution lies in developing adaptive, real-time guidance algorithms that combine the robustness of ZEM-ZEV (Zero-Effort-Miss/Zero-Effort-Velocity) feedback with the flexibility of DRL, enabling spacecraft to autonomously adjust to dynamic environments and uncertainties during descent. This work has significant implications for future missions to the Moon, Mars, and beyond, where real-time decision-making is critical. Beyond this, Scorsoglio has advanced the use of neural networks for optimal control in aerospace systems, demonstrating how AI can enhance safety and efficiency in high-stakes scenarios. Her research is widely cited by engineers and scientists working on autonomous navigation, reflecting its impact on both theoretical foundations and practical applications. Scorsoglio’s achievements have been recognized through multiple grants and collaborations with space agencies, positioning her as a key figure in the next generation of intelligent space exploration.
Research Focus
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Top Papers
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