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Cooperative Path Planning for Multi-USV Based on Improved Artificial Bee Colony Algorithm

机译:基于改进人工蜂群算法的多路无人机协同路径规划

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Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.
机译:针对多种无人水面飞行器(multi-USV)路径规划的复杂约束,不确定因素和关键实时需求,提出了一种改进的人工蜂群算法(I-ABC)来求解协同路径规划模型。用于多级USV。首先,构想战场空间的Voronoi图以生成USV路径的最佳区域。然后,将混沌搜索算法用于初始化路径集合,这被视为ABC算法的基础。由于数据有限,初始集合可以完美地搜索路径的最佳区域。最终,对各种威胁下的多USV路径规划进行了仿真。仿真结果表明,I-ABC算法可以改善花蜜来源的多样性,提高算法的收敛速度。它可以提高动态战场的适应性以及对USV的意外威胁。

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