The optimization of unmanned aerial vehicle (UAV) mission planning has been conducted, taking into account both the task allocation and path planning simultaneously. A dual-feedback mechanism has been introduced into the unmanned aerial vehicle (UAV) mission planning model to address the coupling of issues. To effectively solve the completion rate of tasks and collision avoidance for UAVs, an task planer based on improved Genetic Algorithm has been designed, merging three crossover strategies and the Harris Hawks besiege strategy. The three crossover strategies are employed to enhance the diversity of the population; the Harris Hawks besiege strategy is utilized to intensify local exploitation capabilities in the later stages of the algorithm. In order to verify the performance of the algorithm, it is compared with five other algorithms. The simulation results indicate that the proposed algorithm can effectively solve the UAV mission planning problem.


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    Titel :

    A task planner based on improved genetic algorithm for multi-UAVs attacking multitargets


    Beteiligte:
    Kolhe, Mohan (Herausgeber:in) / Gou, Bin (Herausgeber:in) / Shi, Shuya (Autor:in) / Chai, Xuzhao (Autor:in) / Liu, Linna (Autor:in) / Qian, Pengbo (Autor:in) / Liu, Ping (Autor:in) / Ma, Yanxia (Autor:in)

    Kongress:

    International Conference on Electrical Engineering and Intelligent Systems (IC2EIS 2025) ; 2025 ; Chengdu, China


    Erschienen in:

    Proc. SPIE ; 13696 ; 1369612


    Erscheinungsdatum :

    01.08.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch