基于改进人工鱼群算法的无人机三维航迹规划
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摘要
为解决三维环境下的无人机航迹规划问题,提出一种基于改进人工鱼群算法的无人机航迹规划方法。构建规划空间与威胁模型,依据威胁模型提出导航点自适应选择算法,确定其位置与个数,对航迹进行编码;综合考虑无人机航程、威胁代价、爬升代价以及转弯约束,设计航迹代价函数;采用视野自适应策略改进人工鱼群算法进行航迹寻优,得到航迹。仿真结果表明,与粒子群算法相比,采用改进的人工鱼群算法进行无人机三维航迹规划更有效,代价更小。
To solve the problem of unmanned aerial vehicle(UAV)trajectory planning in the three-dimensional environment,an improved artificial fish swarm algorithm was proposed.The planning space and threaten model were constructed.An adaptive selection algorithm was proposed to determine the location and number of the navigation point,according to the threaten model.On this basis,the trajectory was coded.The comprehensively trajectory cost function of UAV was designed in terms of the airrange,threaten cost,climbing cost and turning constraint.The vision adaptive strategy was adopted to improve artificial fish swarm algorithm for trajectory optimization.Simulation results show that compared with particle swarm algorithm,it is more effective and less cost to use the improved artificial fish swarm algorithm for 3 Dtrajectory planning optimization of UAV.
To solve the problem of unmanned aerial vehicle(UAV)trajectory planning in the three-dimensional environment,an improved artificial fish swarm algorithm was proposed.The planning space and threaten model were constructed.An adaptive selection algorithm was proposed to determine the location and number of the navigation point,according to the threaten model.On this basis,the trajectory was coded.The comprehensively trajectory cost function of UAV was designed in terms of the airrange,threaten cost,climbing cost and turning constraint.The vision adaptive strategy was adopted to improve artificial fish swarm algorithm for trajectory optimization.Simulation results show that compared with particle swarm algorithm,it is more effective and less cost to use the improved artificial fish swarm algorithm for 3 Dtrajectory planning optimization of UAV.
引文
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[4]TANG Biwei,FANG Qun,ZHU Zhanxia,et al.Effective 2D route planning of UAV based on improved ant colony algorithm[J].Journal of Northwestern Polytechnical University,2013,31(5):683-687(in Chinese).[唐必伟,方群,朱战霞,等.基于改进蚁群算法的无人飞行器二维航迹规划[J].西北工业大学学报,2013,31(5):683-687.]
[5]ZHANG Renpeng, YANG Jinxiao, PAN Jiahua,et al.Three-dimensional route planning of UAV based on improved particle swarm algorithm[J].Computer Simulation,2014,31(3):65-68(in Chinese).[张仁鹏,杨金孝,潘佳华,等.基于改进粒子群算法的无人机三维航迹规划[J].计算机仿真,2014,31(3):65-68.]
[6]FANG Qun,XU Qing.3Droute planning for UAV based on improved PSO algorithm[J].Journal of Northwestern Polytechnical University,2017,35(1):66-73(in Chinese).[方群,徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报,2017,35(1):66-73.]
[7]Goez GD,Velasquez RA,Botero JS.On-line route planning of UAV using particle swarm optimization on micro-controllers[J].IEEE Latin America Transactions,2016,14(4):1705-1710.
[8]TANG Huiyu,PENG Shirui,SUN Jingjiao,et al.3Droute planning of UAV based on SAPSO algorithm[J].Tactical Missile Technology,2017(2):62-68(in Chinese).[唐汇禹,彭世蕤,孙经蛟,等.基于SAPSO算法的无人机三维航迹规划[J].战术导弹技术,2017(2):62-68.]
[9]LI Xiaolei.A new intelligent optimization method-artificial fish school algorithm[D].Hangzhou:Zhejiang University,2003(in Chinese).[李晓磊.一种新型的智能优化方法-人工鱼群算法[D].杭州:浙江大学,2003.]
[10]LUAN Xinyuan,LIU Tingzhang,ZHOU Zhuangli.LED color mixing design based on improved artificial fish swarm algorithm[J].Chinese Journal of Luminescence,2015,36(1):113-120(in Chinese).[栾新源,刘廷章,周壮丽.基于改进人工鱼群算法的LED混光方法[J].发光学报,2015,36(1):113-120.]
[11]MA Xianmin,LIU Ni.Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem[J].Journal on Communications,2014,35(1):1-6(in Chinese).[马宪民,刘妮.自适应视野的人工鱼群算法求解最短路径问题[J].通信学报,2014,35(1):1-6.]