A drone port 101 is managed using a machine learning model, the port has at least one level with a landing pad 105, a number of rovers 109 which transport goods from a flying drone landed on the pad to one of a number of lockers 111. Training the machine learning model involves creating a series of constraints to be met by a created digital model of a number of drone ports, and creating a series of rewards when particular environment states are met by the digital model. Constraints may include correctness, energy consumption, drone or drone charge level, or time. Environment states may include position or status of the drones or rovers, or the status of the lockers, such as occupied, open, closed, locked and/or unlocked. The rewards may incur a penalty for environment states such as repetitive movements, delivery of goods to the wrong destination, charge level of drones or rovers being below a threshold. Also claimed is a system for managing a fleet of flying drones operating from a number of the drone ports, having a collaborative server 150 running a machine learning model to communicate with, and control, the drones and the rovers.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Drone port


    Erscheinungsdatum :

    23.11.2022


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Drone port

    KOJIMA TORU / MORI SATOSHI / KUNII SHINYA et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    DRONE PORT

    SOGA TAKAYUKI / TAKAHASHI KATSUYUKI / ONO YUKI et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    DRONE PORT

    SOGA TAKAYUKI / TAKAHASHI KATSUYUKI / ONO YUKI et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    DRONE PORT

    KOJIMA TORU / MORI SATOSHI / KUNII SHINYA et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    DRONE PORT

    MAJOE DENNIS | Europäisches Patentamt | 2025

    Freier Zugriff