东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (3): 312-316.DOI: -

• 信息与控制 • 上一篇    下一篇

基于异步卡尔曼滤波的移动机器人动态定位

吴成东1，冯晟1,2，张云洲1   

  1. (1.东北大学信息科学与工程学院，辽宁沈阳110819；2.东北大学软件学院，辽宁沈阳110819)
  • 收稿日期:2012-03-19 修回日期:2012-03-19 出版日期:2013-03-15 发布日期:2013-01-26
  • 通讯作者: 吴成东
  • 作者简介:吴成东(1960-)，男，辽宁大连人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61203216，61273078)；中央高校基本科研业务费专项资金资助项目(N110404030，N110804004，N110404004，N090304003).

Dynamic Localization of Mobile Robot Based on Asynchronous Kalman Filter

WU Chengdong1， FENG Sheng1,2， ZHANG Yunzhou1   

  1. 1. School of Information Science ＆ Engineering， Northeastern University， Shenyang 110819， China; 2. School of Software， Northeastern University， Shenyang 110819， China.
  • Received:2012-03-19 Revised:2012-03-19 Online:2013-03-15 Published:2013-01-26
  • Contact: FENG Sheng
  • About author:-
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摘要： 针对室内移动机器人动态定位在网络盲区中失效的情况，提出一种根据机器人周围网络环境动态选择信标节点，完成自主定位的系统.利用扩展卡尔曼滤波后的RSSI完成测距，然后采用极大似然算法完成定位，再用异步卡尔曼算法修正定位误差.该算法成功地将经典卡尔曼滤波与其他定位算法相结合，对于定位算法的结果进行平滑和优化，修正和改进定位精度.尤其在网络盲区中，采用异步卡尔曼滤波获得最优数据.仿真实验表明该系统针对移动机器人自主动态定位具有精度高、适应性强、鲁棒性好等特点.

关键词: 无线传感器网络, 动态定位, 改进的极大似然算法, 异步卡尔曼滤波算法, 接收信号强度指标

Abstract: According to the dynamic localization failure of the indoor mobile robot in network blind spots， a selfdynamic localization system was proposed which dynamically choose beacon node on the basis of WSN. The extended Kalman filter was applied to range management by RSSI. Then， maximum likelihood estimation was used to accomplish the localization. Finally， the localization errorcorrect was implemented by asynchronous Kalman filter. In order to correct and improve the localization accuracy， the classical Kalman filter with the other localization algorithms were integrated successfully using the proposed method， which could smooth and optimize the result of the algorithms. Especially in network blind spots， the asynchronous Kalman filter could provide optimal data. Simulation results showed that the accuracy， adaptability and robustness of the selfdynamic localization of mobile robot are good.

Key words: wireless sensor network(WSN), dynamic localization, improved maximum likelihood estimation, asynchronous Kalman filter algorithm, received signal strength index

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