改进免疫算法在无人机航线规划中的应用
|
摘要
针对无人机的航线规划方法展开研究,旨在建立能够融合真实数字地形的,更为客观、合理的航迹规划方法。由于免疫算法易陷入局部最优点及收敛速度过慢等问题,提出了一种基于禁忌准则的改进免疫算法,并应用于无人机航迹规划,其通过基因编码确定个体评价准则、交叉和高频变异等操作,通过在真实的地理环境信息所建立的数字高程地图上进行无人机的初始航迹优化,使航迹能够满足各种约束条件。与蚁群算法对比分析的结果表明,该算法加快了收敛进程,并可求得较优解。
Unmanned aerial vehicle( UAV) path planning method was discussed. It's designed to establish a much more objective and reasonable planned path which could blend with real digital terrain. On account of the slow convergence rate,and that immune algorithm is easily to fall into local optimum,an improved immune algorithm based on tabu criterion was proposed,and it was used to solve the UAV path planning problem. It aimed at determining the individual evaluation criteria through gene encoding and a series of genetic manipulation such as crossover and hyper- mutation. Through the optimization of initial track of UAV on a digital elevation map which was proposed on real geographical information. The flight path could meet various constraints.The comparative analysis with ant colony algorithm shows that the algorithm is faster and more effective to get convergent process and good solutions.
Unmanned aerial vehicle( UAV) path planning method was discussed. It's designed to establish a much more objective and reasonable planned path which could blend with real digital terrain. On account of the slow convergence rate,and that immune algorithm is easily to fall into local optimum,an improved immune algorithm based on tabu criterion was proposed,and it was used to solve the UAV path planning problem. It aimed at determining the individual evaluation criteria through gene encoding and a series of genetic manipulation such as crossover and hyper- mutation. Through the optimization of initial track of UAV on a digital elevation map which was proposed on real geographical information. The flight path could meet various constraints.The comparative analysis with ant colony algorithm shows that the algorithm is faster and more effective to get convergent process and good solutions.
引文
[1]DONG Z N,CHEN Z J,ZHOU R.A hybrid approach of virtual force and A*search algorithm for UAV path replanning[C]∥IEEE Conference on Industrial Electronics and Applications.Beijing:[s.n.],2011:1140-1145.
[2]ZHANG B,GAO X G,RU W.Route planning of UAV based on artificial potential field method[C]∥IEEE.[S.l.]:[s.n.],2011:99-102.
[3]刘金义,刘爽.Voronoi图应用综述[J].Journal of Engineering Graphics,2004(2):125-132.
[4]ZHANG C,ZHEN Z Y,WANG D B,et al.UAV path planning method based on ant colony optimization[C]∥Chinese Control and Decision Conference.[S.l.]:[s.n.],2010:3790-3792.
[5]SZCZERBA R J.Robust algorithm for real-time route planning[J].IEEE Transaction and Aerospace and Electronic Systems,2000(4):869-878.
[6]巴海涛.基于免疫遗传算法的无人机航路规划[J].火力与指挥控制,2007,32(11):27-30.
[7]李清,候永军,沈春林.数字地形数据的二维三次卷积插值[J].南京航空航天大学学报,1997,29(4):378-384.
[8]刘凌宇,彭靖.低空突防仿真用数字地形模型的建立[J].中国体视学与图像分析,2010,15(3):245-250.
[2]ZHANG B,GAO X G,RU W.Route planning of UAV based on artificial potential field method[C]∥IEEE.[S.l.]:[s.n.],2011:99-102.
[3]刘金义,刘爽.Voronoi图应用综述[J].Journal of Engineering Graphics,2004(2):125-132.
[4]ZHANG C,ZHEN Z Y,WANG D B,et al.UAV path planning method based on ant colony optimization[C]∥Chinese Control and Decision Conference.[S.l.]:[s.n.],2010:3790-3792.
[5]SZCZERBA R J.Robust algorithm for real-time route planning[J].IEEE Transaction and Aerospace and Electronic Systems,2000(4):869-878.
[6]巴海涛.基于免疫遗传算法的无人机航路规划[J].火力与指挥控制,2007,32(11):27-30.
[7]李清,候永军,沈春林.数字地形数据的二维三次卷积插值[J].南京航空航天大学学报,1997,29(4):378-384.
[8]刘凌宇,彭靖.低空突防仿真用数字地形模型的建立[J].中国体视学与图像分析,2010,15(3):245-250.