引入导引因子蚁群算法的无人机二维航迹规划OA北大核心CSCDCSTPCD
UAV 2-D Path Planning Based on Ant Colony Optimization Algorithm with Guidance Factor
针对目前采用蚁群算法求解无人机二维航迹规划问题需要构建VORONOI图或设置导航节点的问题,构造了可自动搜索航迹的蚁群算法,解决了该算法应用于航迹规划的两个构造难题:航迹节点不固定及局部搜索到达目标节点困难.并将导引因子引入到状态转移策略当中,减少了蚂蚁局部搜索的盲目性,确保蚂蚁最终完成航迹搜索.此外,采取当前最优航迹信息素更新策略,同时设置信息素上下限,在提高算法速度的同时,防止算法陷入局部最优.仿真结果表明,算法构造非常合理,在没有设置导航节…查看全部>>
At present, it needs to build VORONOI map or set the navigation nodes for getting optimal UAV,two-dimensional path planning by adopting ACO algorithm. A new structure herein of ACO can automatically search flight path. Two construction difficulties were solved. First, path node was not fixed,that is to say it was not a simple combinatorial optimization problem. Second, it was difficult for local search to reach the destination node. And a guidance factor was…查看全部>>
胡中华;赵敏;姚敏
南京航空航天大学,南京,210016南京航空航天大学,南京,210016南京航空航天大学,南京,210016
信息技术与安全科学
蚁群算法航迹规划无人机威胁代价油耗代价导引因子
ant colony optimization (ACO) algorithm path planning unmanned aerial vehicle (UAV) threat cost fuel cost guidance factor
《中国机械工程》 2011 (3)
基于红外视觉的无人飞行器全天候自动精确着陆导引新方法研究
322-325,4
国家自然科学基金资助项目(60974105)航空科学基金资助项目(2009ZC52041)
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