基于改进多目标蚁群算法的无人机路径规划  

UAV path planning using improved multiobjective ant colony system

作　　者：王振华[1] 章卫国[1] 李广文[1] 

机构地区：[1]西北工业大学自动化学院,西安710072

出　　处：《计算机应用研究》2009年第6期2104-2106,2109,共4页Application Research of Computers

摘　　要：针对无人机SEAD任务的路径规划问题,利用VORONOI图构建初始路径,分析了路径代价计算方法,并使用改进的多目标蚁群算法对路径进行优化选择。针对该特殊应用场景,引入了各路径段与起始点—目标点连线的夹角信息作为新的启发信息,加快了算法的搜索速度,同时改进启发信息的计算公式,适当缩小各可选路径段启发信息量的差异,加强了蚁群算法的全局搜索能力。仿真结果显示,与基本多目标蚁群算法相比,改进后的算法有效提高了路径搜索的效率和质量。Based on the VORONOI diagram, this paper calculated the path costs, and then used the multiobjective ant colony system（MACS） algorithm to solve the route planning problem of the UAVs taking the SEAD mission. Calculated and introduced the angles between path segments and the line segment joining the start point and the target point, as heuristic information, into the MACS algorithm to accelerate the searching speed. And also, this paper improved the expression of the heuristic information, which reduced the differences among the path segments, and enhanced the global searching ability of the algorithm. The simulation results show that ： compared with the original MACS, the improved algorithm can find a better result more efficiently.

关 键 词：无人机 路径规划 VORONOI图 多目标蚁群算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]