基于异步卡尔曼滤波的移动机器人动态定位

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.

Dynamic Localization of Mobile Robot Based on Asynchronous Kalman Filter

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.