Space exploration and research have led to many advancements in launch vehicles, landers, and rovers. Conducting in-situ observations requires identifying a safe landing location. To choose a safe landing site, this paper discusses computer vision technology for landers. This study utilizes the YOLOv5n model to identify the moon’s terrains. It is observed in this study that an accuracy of 92% can be achieved with near realtime detection using AI-edge devices.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning Based Real-Time Lunar Terrain Detection for Autonomous Landing Approach


    Beteiligte:


    Erscheinungsdatum :

    2023-12-14


    Format / Umfang :

    1663503 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Deep Learning Approach to Hazard Detection for Autonomous Lunar Landing

    Moghe, Rahul / Zanetti, Renato | Springer Verlag | 2020


    Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

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


    IMAGE-BASED DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS LUNAR LANDING

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


    Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

    Scorsoglio, Andrea / D’Ambrosio, Andrea / Ghilardi, Luca et al. | AIAA | 2021


    Image-based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

    Andrea Scorsoglio / Andrea D’Ambrosio / Luca Ghilardi et al. | BASE | 2021

    Freier Zugriff