摘要
针对无人机在指定地点执行侦察、巡逻或攻击等任务,将无人机执行任务的航迹代价模型转化为旅行商问题,采用改进蚁群算法实现航迹规划。通过引入去交叉禁忌搜索策略,对基本蚁群算法进行改进,以解决在收敛后期易陷入局部最优的问题。同时,利用数值仿真对所研究的基于改进蚁群算法的无人机航迹规划算法进行验证。仿真结果表明,该算法能提高了无人机航迹优化能力。
Aiming at the wide use of the UAV （Unmanned Aerial Vehicles） in designated place to perform reconnaissance, detection or patrol mission, etc, the tasks cost model of the path planning of UAVs is transformed into the traveling salesman problem. The improved ant colony algorithm is employed to complete the path planning of UAV. To overcome the problem of easy convergence to the local optimum in the last stage for the basic ant colony algorithm, the cross tabu search strategy is introduced to improve the basic ant colony algorithm. The simulation study is presented to prove the effectiveness of the developed path planning method based on the improved ant aolony algorithm. The simulation results show that the developed algorithm can improve the optimization ability of path planning for UAV.
出处
《吉林大学学报（信息科学版）》
CAS
2013年第1期66-72,共7页
Journal of Jilin University（Information Science Edition）
基金
航空科学基金资助项目(20105152029)
总装重点实验室类基金资助项目(9140C460202110C4603)
南京航空航天大学基本科研业务费专项科研基金资助项目(2011049)
关键词
无人机
航迹规划
蚁群算法
禁忌搜索
旅行商问题
unmanned aerial vehicles （UAV）
path planning
ant colony algorithm
tabu search
travelling salesman problem （TSP）
