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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    Title :

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


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jiang, Guo-Ping (editor) / Wang, Mengyi (editor) / Ren, Zhang (editor) / Zhang, Yibo (author) / Han, Liang (author) / Li, Xiaoduo (author) / Yu, Haoyang (author) / Ren, Zhang (author)

    Conference:

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



    Publication date :

    2024-07-12


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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