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

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

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.