Multi-agent sensing for transportation systems is receiving widespread attention due to its dynamic flexibility and collaborative capabilities, where the target sensing error is limited by the spatio-temporal error caused by agent localization and formation steps. This paper considers the sensing problem of non-cooperative targets (UAVs or vehicles) by cooperative asynchronous agents (UAVs). This paper develops a framework where the formation of agents and the allocation of resources are jointly optimized. In particular, we reveal the error coupling of measurement and motion noises on target sensing accuracy by Fisher information analysis. Then we propose bandwidth allocation and agent activation strategies in the localization step, which simultaneously improve the position accuracy of agents and the quality of sensing signals. In the formation step, we design motion planning algorithms to increase sensing information about targets. Simulation results demonstrate the significant performance improvements achieved by our proposed algorithms that minimize the effects of localization and control errors on target sensing.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Multi-Agent Sensing Framework via Joint Motion Planning and Resource Optimization


    Beteiligte:
    Ma, Kai (Autor:in) / Liu, Zhenyu (Autor:in) / Shen, Yuan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    4734253 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    CASPNet++: Joint Multi-Agent Motion Prediction

    Schafer, Maximilian / Zhao, Kun / Kummert, Anton | IEEE | 2024



    Kinodynamic Motion Planning Using Multi-Objective optimization

    Hart, Patrick / Knoll, Alois | IEEE | 2018


    KINODYNAMIC MOTION PLANNING USING MULTI-OBJECTIVE OPTIMIZATION

    Hart, Patrick / Knoll, Alois | British Library Conference Proceedings | 2018


    Space-Time Conflict Spheres for Constrained Multi-Agent Motion Planning

    Chari, Anirudh / Chen, Rui / Liu, Changliu | IEEE | 2023