引用本文:方 正,佟国峰,徐心和.基于粒子群优化的粒子滤波定位方法[J].控制理论与应用,2008,25(3):533~537.[点击复制]
FANG Zheng,TONG Guo-feng,XU Xin-he.A localization method for particle-filter based on the optimization of particle swarm[J].Control Theory and Technology,2008,25(3):533~537.[点击复制]
基于粒子群优化的粒子滤波定位方法
A localization method for particle-filter based on the optimization of particle swarm
摘要点击 3047  全文点击 2478  投稿时间：2006-02-21  修订日期：2007-01-23
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DOI编号  10.7641/j.issn.1000-8152.2008.3.027
  2008,25(3):533-537
中文关键词  移动机器人  自定位  粒子滤波  粒子群优化
英文关键词  mobile robot  self-localization  particle filter  particle swarm optimization
基金项目  国家自然科学基金资助项目(60475036).
作者单位
方 正 东北大学 教育部暨辽宁省流程工业综合自动化重点实验室, 辽宁 沈阳 110004 
佟国峰 东北大学 人工智能与机器人研究所, 辽宁 沈阳 110004 
徐心和 东北大学 人工智能与机器人研究所, 辽宁 沈阳 110004 
中文摘要
      为了实现移动机器人精确高效的自定位, 提出了基于粒子群优化的粒子滤波定位方法. 文章分析了常规粒子滤波定位方法存在的不足之处. 将最新观测值融合到采样过程中, 并利用粒子群优化算法提高了常规粒子滤波器的预估性能. 接下来, 建立了系统的概率运动模型和感知模型,并利用粒子群优化粒子滤波方法解决了移动机器人的自定位问题. 粒子群优化算法的优化结果使得采样集向后验概率密度分布取值较大的区域运动, 从而克服了粒子贫乏问题并且显著地降低了精确定位所需的粒子数. 仿真实验表明该算法的有效性.
英文摘要
      To locate a mobile robot efficiently and accurately, we propose a localization algorithm for the particlefilter based on particle swarm optimization. The drawbacks of generic particle- filter are analyzed. By incorporating the newest observations into the sampling process and using particle swarm optimization, the prediction performance of the generic particle-filter is improved. After that, the probabilistic motion-model and observation-model of the mobile robot are established, and the self-localization problem of the mobile robot is resolved by applying the particle swarm optimization to the particle filter. In this method, through particle swarm optimization, particles are moved to the regions where they have larger values of posterior density function. As a result, the impoverishment of the particle filter is overcome and the number of particles needed for accurate location is reduced dramatically. Simulation experiments show the validity of the proposed method.