文档介绍：第16卷第4期 2009年4月电光岛控制 Electronics Optics&Control 基于自适应变异粒子群算法的无人机航迹规划黄因荣,张玄广,捌华伟(空军工程大学工程学院,西安710038) 接要:提出一种基予鸯适应粒子群算法的航避艉楚Il方法。航迹兢瓣是低空突防过程中砖关键技术,拜酶建祥载一每既安全可靠叉全局代价最优的三雏航速。针对熬本粒子群优化算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,采用鑫适应粒子群优化算法,仿真结暴表明该方浓能够快速有效地完成规划任务,获得满意的三维航速。关键词:芄人辆;虢遮兢楚ll;蠹遗应粒子薛;低空突跨中图分类母:V279 文献标志码:A 文章编号:1671—637X(2009)04-0018一04 Route Planning forUnmanned Aerial Vehicles Based on Adaptive Mutation Particle Swarm Optimization HUANG Guorong,ZHANG Jiguang,LIU Huawei (嘲肥e峨College,Air Force Engineering University,Xi’811710038,China) Abstract:Flight route planning is&key technology inlow—ration,which usedforobtaining asafe,reliable and global optimal three—dimensional flight new route planning method was put forward based on 811adaptive mutation particle swarm optimization。The basicparticle swarm optimization has some defeets,such as being liable toget intolocalextremum,slow convergence velocity and low convergence precision inthe adaptive mutation particle SWal3flloptimization Can e thedefectsofthebasicparticle swarm optimization。The simulation resultsdemonstratedthat this method can implement route planning rapidly and efficiently andget adesired 30path. Key words:unmanned aerialvehicle;route planning;adaptive particleswarm;low—ration 0引言 Kennedy冬Eberhart递过黯鸟群觅食过程戆分析并模拟,于1995年最先提出了原始的粒子群优化(PSO)算法¨-2]。PSO算法具有原理简单、易于实现、参数较少、效率较意等突进优点,所