基于AFK的UWB与IMU融合定位示教/再现导航算法研究

毕涛

基于AFK的UWB与IMU融合定位示教/再现导航算法研究

投稿时间：2025-10-14 修订日期：2025-10-28 点此下载全文

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作者	单位	邮编
毕涛^*	国网江苏省电力有限公司	211189

基金项目:国网江苏省电力有限公司孵化项目（编号：JF2025006）

中文摘要:本文提出一种基于超宽带（UWB）与惯性传感器（IMU）融合的自适应卡尔曼滤波（AFK）定位的示教/再现导航算法，首先构建了自动导引车（AGV）运动学与UWB测量模型，然后设计了双重自适应噪声协方差调整策略，通过新息序列卡方检验动态优化测量噪声协方差R，依据IMU感知的运动状态及UWB信号丢失情况自适应调整过程噪声协方差Q，解决传统卡尔曼滤波参数固定的缺陷。基于以上滤波算法搭建软硬件系统，设计了示教/再现导航算法，示教阶段手动操控AGV获取示教轨迹，再现阶段通过分段PI控制完成对示教轨迹的精确跟踪。实验表明，改进后示教/再现轨迹具有良好的鲁棒性与轨迹跟踪精度。

中文关键词:自适应卡尔曼滤波 UWB与IMU融合示教/再现 AGV导航

Research on Teaching/Playback Navigation Algorithm for UWB and IMU Fusion Positioning Based on Adaptive Kalman Filter

Abstract:A teaching/playback navigation algorithm for the ultra wide band (UWB) and the inertial measurement unit (IMU) fusion positioning based on the adaptive kalman filter (AKF) is proposed in this paper. Firstly, the automated guided vehicle (AGV) kinematics and the UWB measurement models are constructed. Then, a dual adaptive noise covariance adjustment strategy is designed: the measurement noise covariance?R?is dynamically optimized through the chi-square test of the innovation sequence, and the process noise covariance?Q?is adaptively adjusted according to the motion state sensed by the IMU and the UWB signal loss status. This addresses the limitation of fixed parameters in traditional Kalman Filter. A software and hardware system is built based on the above filtering algorithm, and a teaching/playback navigation algorithm is designed. In the teaching phase, the AGV is manually controlled to acquire the teaching trajectory. In the playback phase, the segmented PI control is used to achieve accurate tracking of the teaching trajectory. Experiments show that the improved teaching/playback algorithm has excellent robustness and trajectory tracking accuracy.

keywords:Adaptive Kalman Filter UWB and IMU Fusion Teaching/Playback AGV Navigation

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