1–20 von 26 Ergebnissen
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    Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach

    Furfaro, Roberto / Scorsoglio, Andrea / Linares, Richard et al. | Elsevier | 2020

    Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | AIAA | 2020

    A Physic-Informed Neural Network Approach to Orbit Determination

    Scorsoglio, Andrea / Ghilardi, Luca / Furfaro, Roberto | Springer Verlag | 2023

    Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters

    Holt, Harry / Armellin, Roberto / Scorsoglio, Andrea et al. | AIAA | 2020

    Meta-reinforcement learning for adaptive spacecraft guidance during finite-thrust rendezvous missions

    Federici, Lorenzo / Scorsoglio, Andrea / Zavoli, Alessandro et al. | Elsevier | 2022

    Onboard State Estimation Around Didymos With Recurrent Neural Networks and Segmentation Maps

    Pugliatti, Mattia / Scorsoglio, Andrea / Furfaro, Roberto et al. | IEEE | 2024

    Relative motion guidance for near-rectilinear lunar orbits with path constraints via actor-critic reinforcement learning

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | Elsevier | 2022

    Physics-Informed Neural Networks for Closed-Loop Guidance and Control in Aerospace Systems

    Furfaro, Roberto / D'Ambrosio, Andrea / Schiassi, Enrico et al. | TIBKAT | 2022

    Physics-Informed Neural Networks for Closed-Loop Guidance and Control in Aerospace Systems

    Furfaro, Roberto / D'Ambrosio, Andrea / Schiassi, Enrico et al. | AIAA | 2022

    Autonomous Guidance Between Quasiperiodic Orbits in Cislunar Space via Deep Reinforcement Learning

    Federici, Lorenzo / Scorsoglio, Andrea / Zavoli, Alessandro et al. | AIAA | 2023

    LOW-THRUST TRAJECTORY DESIGN USING CLOSED-LOOP FEEDBACK-DRIVEN CONTROL LAWS AND STATE-DEPENDENT PARAMETERS

    Holt, Harry / Armellin, Roberto / Scorsoglio, Andrea et al. | TIBKAT | 2020

    META-REINFORCEMENT LEARNING FOR ADAPTIVE SPACECRAFT GUIDANCE DURING MULTI-TARGET MISSIONS

    Federici, Lorenzo / Scorsoglio, Andrea / Zavoli, Alessandro et al. | TIBKAT | 2022

    IMAGE-BASED DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS LUNAR LANDING

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | TIBKAT | 2020

    Actor-Critic Reinforcement Learning Approach to Relative Motion Guidance in Near-Rectilinear Orbit

    Scorsoglio, Andrea / Furfaro, Roberto / Linares, Richard et al. | TIBKAT | 2019

    Orbit determination pipeline for geostationary objects using physics-informed neural networks

    Scorsoglio, Andrea / D'Ambrosio, Andrea / Campbell, Tanner et al. | AIAA | 2024

    Machine Learning-based Light Curves Brightness Prediction for Space Objects in the Geostationary Belt

    D'Ambrosio, Andrea / Scorsoglio, Andrea / Battle, Adam et al. | AIAA | 2024

    Deep Imitation Learning and Clustering in Astrodynamics (AAS 19-700)

    Furfaro, Roberto / Drozd, Kristofer / Linares, Richard et al. | TIBKAT | 2020

    Optimal Q-laws via reinforcement learning with guaranteed stability

    Holt, Harry / Armellin, Roberto / Baresi, Nicola et al. | Elsevier | 2021