With the ongoing development of intelligent technologies, fixed-wing unmanned aerial vehicles (UAVs) are gaining prominence across diverse sectors. This study focuses on complex environment exploration. It employs the Dyna-Q reinforcement learning algorithm to derive optimal waypoints in obstacle-rich settings. These waypoints are pivotal for task allocation, ensuring judicious deployment of UAV clusters for targeted exploration. Concurrently, B-spline techniques refine these waypoints into feasible flight trajectories for UAVs. Subsequently, distributed model predictive control (DMPC) is utilized to maintain a designated cluster configuration. This DMPC controller ensures trajectory tracking along optimized paths, facilitating thorough area exploration. Then the approach is validated and evaluated through simulation experiments using a laboratory-developed simulation platform.


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    Titel :

    Enhancing Exploration Efficiency of Fixed-Wing UAVs Through Intelligent Decision-Making and Advanced Control Integration


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jiang, Guo-Ping (Herausgeber:in) / Wang, Mengyi (Herausgeber:in) / Ren, Zhang (Herausgeber:in) / Zhang, Yibo (Autor:in) / Han, Liang (Autor:in) / Li, Xiaoduo (Autor:in) / Yu, Haoyang (Autor:in) / Ren, Zhang (Autor:in)

    Kongress:

    Chinese Conference on Swarm Intelligence and Cooperative Control ; 2023 ; Nanjing, China November 24, 2023 - November 27, 2023



    Erscheinungsdatum :

    12.07.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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