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无人机(UCAV)是自主控制执行任务的无人驾驶飞机,其航路规划是一类复杂优化问题,因此难以在多项式时间内获取精确解,为此提出了一种基于Voronoi图和量子粒子群(QPSO)算法的UCAV航路规划方法。首先,在综合考虑航路的雷达威胁和燃油耗费的基础上定义了航路规划的代价模型;然后,根据已知的威胁源生成Voronoi图,通过连接起点、Voronoi图中顶点以及终点获得初始规划解集;最后,通过引入柯西变异随机数和扰动对QPSO算法进行改进,以增强其全局寻优能力和收敛速度,并定义了采用此改进的QPSO算法对UCAV进行最终航路规划的具体算法。仿真实验表明,该方法能求解出UCAV航路规划的最优解,且与经典的PSO算法和QPSO算法相比,具有全局寻优能力强和收敛速度快的优点。
Unmanned aerial vehicle (UCAV) is a drones that independently control the implementation of the mission, its route planning is a complex optimization problem, so it is difficult to obtain exact solutions in polynomial time. To this end, a Voronoi diagram and quantum particle swarm (QPSO) algorithm UCAV route planning method. First, the cost model of route planning is defined based on comprehensive consideration of the radar threat and fuel consumption of the route; then, a Voronoi map is generated based on the known threat sources, the initial solution set is obtained by connecting the starting point, the vertices in the Voronoi diagram, and the ending point Finally, the QPSO algorithm is improved by introducing the Cauchy mutation random number and perturbation to enhance its global optimization ability and convergence speed, and the specific algorithm for the final route planning of UCAV by using this improved QPSO algorithm is defined. Simulation results show that this method can solve the optimal solution of UCAV route planning, and has the advantages of global optimization ability and fast convergence compared with classical PSO algorithm and QPSO algorithm.