This paper investigates Multi-Agent Systems (MAS) related data analytics. MAS is used for modeling complex, decentralized, and real-world tasks such as package delivery by Unmanned Aircraft Systems (UAS), environmental monitoring, precision agriculture, security, disaster management, UAS Traffic Management (UTM) among others. Fish farming is one such area, where the deployment of UAS platforms could drastically improve the current labor-intensive and resource-constraint operations. This research addresses the design of mission control and path planning strategies for UASs deployed on the fish farm. The proposed strategy enables periodic monitoring of mission-critical parameters. A control strategy is designed to address the tracking control of the UAS under wind conditions.


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    Titel :

    Multi-Agent Systems (MAS) related data analytics in the Hybrid Aerial Underwater Robotic System (HAUCS)


    Beteiligte:
    Mukherjee, Srijita (Autor:in) / Ouyang, Bing (Autor:in) / Namuduri, Kamesh (Autor:in) / Wills, Paul S. (Autor:in)

    Kongress:

    Big Data III: Learning, Analytics, and Applications ; 2021 ; Online Only,Florida,United States


    Erschienen in:

    Proc. SPIE ; 11730


    Erscheinungsdatum :

    12.04.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Aerial robotic data acquisition system

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