基于粒子群优化的小型无人机FastSLAM算法研究  

FastSLAM Algorithm for SUAV Based on Particle Swarm Optimization Algorithm

作　　者：陈含欣[1] 李志宇[1] 王从庆[1] 

机构地区：[1]南京航空航天大学,南京210016

出　　处：《电光与控制》2016年第9期24-28,54,共6页Electronics Optics & Control

基　　金：江苏省科技支撑计划(BE2014712;BE2010190)

摘　　要：针对在无GPS环境下的小型无人机自主飞行问题,提出了一种基于改进的粒子群优化(IPSO)的快速同步定位和地图创建(FastSLAM)方法—IPSO FastSLAM算法。IPSO算法通过引入自适应重采样方法,有效克服了粒子退化问题,又减少了过频重采样带来的计算负担。提出了以粒子集多样性测度作为启发因子,引导重采样后的粒子优化搜索过程,克服重采样过程造成的样本枯竭问题,保证粒子集多样性水平最优。最后,应用该算法对一种小型无人机模型进行仿真实验,结果验证了该算法正确、可行。For the autonomous flight of Small Unmanned Aerial Vehicle（ SUAV） without GPS,a fast Simultaneous Location and Mapping（ FastSLAM） based on Improved Particle Swarm Optimization algorithm（ IPSO） ,i. e. ,IPSO FastSLAM,is presented. An adaptive resampling method is introduced into the proposed algorithm to effectively overcome the degradation of particles and reduce calculation burden on frequency resampling. But resampling process may cause sample depletion,thus diversity measure of particle sets is put forward as an inspiration factor,leading the particles optimization search process after resampling,to ensure diversity particle set optimal. Finally,simulation research is carried out by using the proposed algorithm on SUAV,and result shows that this method can get better performance on estimation accuracy and feasibility.

关 键 词：小型无人机 自主飞行 粒子群优化 同步定位与地图构建 自适应重采样 

分 类 号：V279[航空宇航科学与技术—飞行器设计]