This article presents a hybrid approach to enhance the path planning for unmanned ground vehicles (UGVs) by combining stochastic dynamic programming (SDP) based hybrid model predictive control (HMPC) with Dijkstra-based pseudo priority queues (PPQ). The proposed approach employs a platform-specific model that takes into account skid-slip effects, a global cost-to-go (CTG) function, and dynamic obstacles. To achieve efficient path planning on large maps, the proposed approach employs a Dijkstra algorithm and PPQ to generate the CTG function. Moreover, the HMPC process incorporates the latest CTG information, the vehicle model, and the perceived environment, encompassing both static and dynamic obstacles. Extensive simulations have been conducted to comprehensively evaluate the performance of the proposed technique. Through a comparative analysis against existing path planners, the results demonstrate the effectiveness and superiority of the hybrid approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hybrid Model Predictive Control for Unmanned Ground Vehicles


    Beteiligte:
    Khan, Subhan (Autor:in) / Guivant, Jose (Autor:in) / Li, Yonghui (Autor:in) / Liu, Wanchun (Autor:in) / Li, Xuesong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2024-01-01


    Format / Umfang :

    7135440 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Research on the Model Predictive Trajectory Tracking Control of Unmanned Ground Tracked Vehicles

    Shuai Wang / Jianbo Guo / Yiwei Mao et al. | DOAJ | 2023

    Freier Zugriff


    Distributed Model Predictive Control for Unmanned Aerial Vehicles

    Mansouri, Sina Sharif / Nikolakopoulos, George / Gustafsson, Thomas | BASE | 2015

    Freier Zugriff


    Nonlinear Model Predictive Control Technique for Unmanned Air Vehicles

    Nathan Slegers / Jason Kyle / Mark Costello | AIAA | 2006