Decentralized control methods are appealing in coordination of multiple vehicles due to their low demand for long-range communication and their robustness to single-point failures. An important approach in decentralized multi-vehicle control involves artificial potentials or digital pheromones. In this paper we explore a decentralized approach to path generation for a group of combat vehicles in a battlefield scenario. The mission is to maneuver the vehicles to cover a target area. The vehicles are required to maintain good overall area coverage, and avoid obstacles and threats during the maneuvering. The gradient descent method is used, where each vehicle makes its moving decision by minimizing a potential function that encodes information about its neighbours, obstacles, threats and the target. We conduct analysis of vehicle behaviors by studying the vector field induced by the potential function. Simulation has shown that this approach leads to interesting emergent behaviors, and the behaviors can be varied by adjusting the weighting coefficients of different potential function terms.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Decentralized Control of Autonomous Vehicles


    Beteiligte:
    J. S. Baras (Autor:in) / X. Tan (Autor:in) / P. Hovareshti (Autor:in)

    Erscheinungsdatum :

    2003


    Format / Umfang :

    16 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




    Decentralized Path Planning for Cooperating Autonomous Vehicles

    Rothfuß, S. / Prezdnyakov, R. / Flad, M. et al. | British Library Conference Proceedings | 2019


    Decentralized cloud-based authentication for autonomous vehicles

    HASSANI ALI | Europäisches Patentamt | 2020

    Freier Zugriff

    DECENTRALIZED CLOUD-BASED AUTHENTICATION FOR AUTONOMOUS VEHICLES

    HASSANI ALI | Europäisches Patentamt | 2020

    Freier Zugriff

    Towards Biologically Inspired Decentralized Platooning for Autonomous Vehicles

    Sreenivasamurthy, Shesha / Obraczka, Katia | IEEE | 2021