As the situation of unmanned combat changes rapidly, it is necessary to carry out dynamic task planning. How to conduct dynamic task scheduling for distributed intelligent unmanned combat systems has become a hot and difficult problem. In order to solve this problem, this paper proposes a method of fusing multi-attribute decisions under the framework of multi-objective optimization by using the characteristics of elastic networks, which can generate scheduling policies quickly when the execution sequence of input tasks is finite. Secondly, a multi-attribute decision-making method combining filter and Pearce correlation coefficient is set up to select the optimal compromise scheme on the Pareto surface. Finally, an experimental case of UAV group rescue is designed to obtain the Pareto front of the problem, and the optimal compromise scheme is selected on the Pareto surface to verify the effectiveness of the proposed method.


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    Title :

    Multi-objective Optimization and Multi-attribute Decision Making Method of UAV Swarm Based on Elastic Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Duan, Ting (author) / Huang, Meigeng (author) / Wang, Weiping (author) / Li, Xiaobo (author) / Wang, Tao (author) / Li, Bing (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    18 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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