This paper addresses the problem of detecting possible misbehavior in a group of autonomous mobile robots, which coexist in a shared environment and interact with each other and coordinate according to a set of common interaction rules. Such rules specify what actions each robot is allowed to perform in order to interact with the other members of the group. The rules are distributed, i.e., they can be evaluated only starting from the knowledge of the individual robot and the information the robot gathers from neighboring robots. We consider misbehaving those robots which, because of either spontaneous failures or malicious tampering, do not follow the rules and whose behavior thus deviates from the nominal assigned one. The main contribution of the paper is to provide a methodology to detect such misbehavior by observing the congruence of actual behavior with the assigned rules as applied to the actual state of the system. The presented methodology is based on a consensus protocol on the events observed by robots. The methodology is fully distributed in the sense that it can be performed by individual robots based only on the local available information, it has been theoretically proven and validated with experiments involving real aerial heterogeneous robots.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Distributed motion misbehavior detection in teams of heterogeneous aerial robots



    Erscheinungsdatum :

    2015-01-01



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    MISBEHAVIOR DETECTION IN AUTONOMOUS DRIVING COMMUNICATIONS

    YANG LIUYANG LILY / SASTRY MANOJ R / LIU XIRUO et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Edge Assisted Misbehavior Detection for Platoons

    Kan, Xinyue / Ganlath, Akila / Ucar, Seyhan et al. | IEEE | 2019


    Misbehavior detection in autonomous driving communications

    YANG LIUYANG LILY / SASTRY MANOJ R / LIU XIRUO et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    CONTEXT-ADAPTIVE RSSI-BASED MISBEHAVIOR DETECTION

    CHEN CONG / PETIT JONATHAN / ANSARI MOHAMMAD RAASHID | Europäisches Patentamt | 2022

    Freier Zugriff