| 引用本文: | 陈少斌,蒋静坪.运用异质传感器信息融合的移动机器人自定位[J].控制理论与应用,2008,25(5):883~886.[点击复制] |
| CHEN Shao-bin,JIANG Jing-ping.Self-localization of the mobile robot utilizing the heterogeneous sensor information fusion[J].Control Theory and Technology,2008,25(5):883~886.[点击复制] |
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| 运用异质传感器信息融合的移动机器人自定位 |
| Self-localization of the mobile robot utilizing the heterogeneous sensor information fusion |
| 摘要点击 1253 全文点击 1099 投稿时间：2006-10-06 修订日期：2007-10-24 |
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| DOI编号 10.7641/j.issn.1000-8152.2008.5.016 |
| 2008,25(5):883-886 |
| 中文关键词 移动机器人 扩展卡尔曼滤波 神经网络 信息融合 自定位 |
| 英文关键词 mobile robot extended Kalman filter neural network information fusion self-localization |
| 基金项目 国防科技预研基金资助项目(J16.6.3). |
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| 中文摘要 |
| 采用单类、单一传感器很难获得移动机器人的准确定位. 为此, 运用异质传感器信息融合来提高定位精度.首先, 建立机器人运动方程和CCD摄像机观测模型. 然后, 利用扩展卡尔曼滤波器进行状态估计, 选择Q, R矩阵抑制系统的模型噪声和量测噪声, 并实现移动机器人的自定位. 接着, 建立超声波传感器的观测模型, 获得机器人的自定位信息. 最后, 运用BP神经网络, 将两种自定位信息进行融合, 实现两类传感器的优缺点互补. 仿真实验表明,运用异质传感器信息融合能明显地提高移动机器人的自定位精度. |
| 英文摘要 |
| It is difficult to realize the exact self-localization of mobile robot by using a single type sensor. The heterogeneous
sensor information fusion is utilized to improve the self-localization precision. First, the motion model of the mobile
robot and observed model of CCD vidicon are established. The optimal state estimation is derived, model disturbances
and measurement noises are restrained by the Q;R matrices, and the self-localization is realized by the extended Kalman
filter. Then, the observed model of the ultrasonic sensor is established, and the self-localization information is obtained.
Finally, the data from CCD vidicon and the ultrasonic sensor are fused by BP neural network. The cooperation of the two
types of sensors is realized. The simulation results show that the self-localization precision of the mobile robot is obviously
improved by the heterogeneous sensor information fusion. |
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