This article proposes a coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of the following two steps: first, impact point optimization and, second, model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles


    Beteiligte:
    Choi, Joonwon (Autor:in) / Seo, Minguk (Autor:in) / Shin, Hyo-Sang (Autor:in) / Oh, Hyondong (Autor:in)


    Erscheinungsdatum :

    01.12.2022


    Format / Umfang :

    6685762 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Delivering Newspapers Using Fixed Wing Unmanned Aerial Vehicles

    Agarwal, Varun / Tewari, Rajiv Ranjan | Springer Verlag | 2021


    Flocking Algorithm for Fixed-Wing Unmanned Aerial Vehicles

    Kownacki, Cezary / Ołdziej, Daniel | Springer Verlag | 2015



    Bridging GPS Outages for Fixed-wing Unmanned Aerial Vehicles

    Zhao, Wenjie / Fang, Zhou / Li, Ping | Tema Archiv | 2015


    Motion Planning of Multiple Fixed-Wing Unmanned Aerial Vehicles in 3 Dimension

    Chand, Roneel / Raghuwaiya, Krishna / Vanualailai, Jito et al. | Springer Verlag | 2024