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针对多无人机静态搜索任务与持续监视任务开展多无人机协同搜索策略研究。针对基于“回报率”图的贪婪搜索策略和分布式协议搜索策略在搜索收益和剩余“回报率”均匀性方面存在的不足,对其原因进行了分析并提出了基于模糊c均值聚类的多无人机协同搜索策略,该策略将模糊数学的方法引入多无人机协同搜索领域,将离散搜索环境以“回报率”和空间位置为特征矢量进行聚类划分,减少了无人机转场次数,降低了搜索代价的无功消耗,提高了搜索收益和剩余“回报率”均匀性。仿真结果表明,本文提出的协同搜索策略在静态搜索任务中比贪婪策略的搜索收益提升了39.03%,剩余“回报率”降低80%,比分布式协议搜索策略性能有所提升;而在持续监视任务中,本文提出的搜索策略的搜索收益比两种基准搜索策略提升了22.74%和18.82%,剩余“回报率”均匀性分别提升了89.28%和87.37%,有效地提高了多无人机协同搜索的任务效能。
Research on multi-UAV collaborative search strategy for multi-UAV static search tasks and continuous surveillance tasks. In view of the shortcomings of the greedy search strategy and the distributed protocol search strategy based on “rate of return ” graph in terms of search yield and remaining “return ” uniformity, the reasons for this are analyzed and the fuzzy c-means Clustering multi-UAV collaborative search strategy. This strategy introduces the fuzzy mathematics method into the multi-UAV collaborative search. The discrete search environment is clustered by using the feature of “return The number of UAV transitions reduced the reactive power consumption of search costs and improved the search yield and remaining ”return“ uniformity. The simulation results show that the proposed collaborative search strategy improves the search revenue of the greedy strategy by 39.03% and the remaining ”return rate“ by 80% in the static search task, which improves the performance of the distributed protocol search strategy. In the continuous monitoring task, the search returns of the proposed search strategy improved by 22.74% and 18.82% respectively compared with the two benchmark search strategies, and the remaining ”return" uniformity increased by 89.28% and 87.37% respectively, effectively increasing Mission effectiveness of UAV collaborative search.