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。

Path routing for Unmanned Combat Aerial Vehicle (UCAV) can be defined as the task of Unmanned Combat Aerial Vehicle automatically executing, which is a complex optimization problem. It is very hard to get the optimal solution in polynomial time. Therefore, in this paper a path planning method was proposed based on Voronoi diagram and Quantum-behaved Particle Swarm Optimization (QPSO) algorism. Firstly, the cost model for path planning of UCAV was defined by totally consideration for the radar threat and fuel consumption, and the Voronoi diagram was generated according to the given threat source. And then the initial path planning set was constructed by initial sites, the vertex of Voronoi diagram and the final sites. Finally, in order to conquer the problem of PSO algorism that has the defects of falling to optimal location, the Cauchy mutation random number was introduced to improve the global search ability of QPSO algorism, and using the improved QPSO algorism to plan path the specific algorism was defined. The result of simulation experiment shows the method proposed in this paper can obtain the optimal solution for UCAV, and it has the optimal cost 280 in comparison with PSO 600 and QPSO 350, respectively. Meanwhile, at the mean time, when the iteration time is 250, the improved QPSO in our paper is in convergence, so it can provide not only the optimal solution but also the rapid convergence speed. Thus it has big superiority over the other methods.