Unmanned aerial vehicles (UAVs) are increasingly being utilized for a wide variety of applications. However, malicious or illegal UAV (drone) activity poses great challenges for public safety. To address such challenges, this work proposes a framework based on reinforcement learning (RL) in which multiple UAVs cooperatively jam multiple rogue drones in flight in order to safely disable their operation. The main objective is to select mobility and power level control actions for each UAV to best jam the rogue drones, while also accounting for the interference power received by surrounding communication systems. Simulation experiments are conducted to evaluate the performance of the proposed approach, demonstrating its effectiveness and advantages as compared to a centralized solution.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multi-Agent Reinforcement Learning for Multiple Rogue Drone Interception


    Beteiligte:


    Erscheinungsdatum :

    06.06.2023


    Format / Umfang :

    2152559 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-Agent Coordinated Interception of Multiple Rogue Drones

    Valianti, Panayiota / Papaioannou, Savvas / Kolios, Panayiotis et al. | IEEE | 2020


    Autonomous Drone Interception with Reinforcement Learning

    Gauffriau, Adrien / Grasset, Damien / Bertoin, David et al. | TIBKAT | 2022

    Freier Zugriff

    Drone interception

    ROBERT SALTER | Europäisches Patentamt | 2024

    Freier Zugriff

    DRONE INTERCEPTION

    SALTER ROBERT JOHN | Europäisches Patentamt | 2021

    Freier Zugriff

    DRONE INTERCEPTION

    SALTER ROBERT JOHN | Europäisches Patentamt | 2023

    Freier Zugriff