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About

Davide Guzzetti is a researcher at the forefront of astrodynamics and autonomous spacecraft systems, with contributions spanning trajectory design in multi-body dynamical environments and artificial intelligence-driven space mission autonomy. His foundational work, "An Earth–Moon System Trajectory Design Reference Catalog" (2014, 41 citations), established a comprehensive framework for mission planners navigating the complex gravitational dynamics of cislunar space, cementing his role as a key contributor to lunar trajectory research during a pivotal period of renewed interest in Moon exploration. Guzzetti has since pioneered the application of reinforcement and imitation learning to spacecraft path-planning challenges, particularly for on-orbit servicing, assembly, and manufacturing (OSAM) missions. His highly cited 2021 paper on reinforcement learning for smart imaging of uncooperative space objects (29 citations) demonstrated how autonomous guidance systems can reduce mission cost and risk in uncertain orbital environments. Subsequent work exploring deep reinforcement learning for fly-around guidance and generalized imitation-based planning strategies further extends this vision, addressing epistemic uncertainty in three-body dynamical regimes. Collectively, Guzzetti's research bridges classical astrodynamics with cutting-edge machine learning, making him a distinctive voice in the emerging field of intelligent autonomous spacecraft operations.