Autonomous unmanned aerial vehicle (UAV) can be utilized to replace humans to do hard work or work in dangerous environment. Path planning is one of the crucial technologies for intelligent flight of UAV. The Rapidly-exploring Random Tree (RRT) has a wide range of applications in path planning with the advantage of the sampling-based path planning which avoids complex construction of the configuration space. However, these methods perform a uniform random sampling and thus do not lead to the most efficient solution. In this paper, in order to improve the efficiency of the traditional RRT, a heuristic strategy with goal-bias was exploited and incorporated in the traditional RRT. A simulation platform was built to assess the performance of the improved algorithm and analyze the influences of the parameters, and a flight test platform was established based on the DJI Matrice 100 to carry out the flight experiments of UAV path planning. The simulations proved that the heuristic RRT is much higher efficient than the standard version. The experiments validated the feasibility to apply the new RRT in the navigation for UAVs in reality.


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    Titel :

    An Efficient Heuristic Rapidly-Exploring Random Tree for Unmanned Aerial Vehicle


    Weitere Titelangaben:

    Lecture Notes on Data Engineering and Communications Technologies


    Beteiligte:
    Xiong, Ning (Herausgeber:in) / Li, Maozhen (Herausgeber:in) / Li, Kenli (Herausgeber:in) / Xiao, Zheng (Herausgeber:in) / Liao, Longlong (Herausgeber:in) / Wang, Lipo (Herausgeber:in) / Yin, Chunping (Autor:in) / Lin, Meijin (Autor:in) / Liu, Qun (Autor:in) / Zhu, Hongmei (Autor:in)

    Kongress:

    The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery ; 2022 ; Fuzhou, China July 30, 2022 - August 01, 2022



    Erscheinungsdatum :

    30.01.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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