移动机器人的卡尔曼滤波定位算法改进与仿真

Improvement and Simulation of Kalman Filter Localization Algorithm for Mobile Robot

针对传统卡尔曼滤波算法和扩展卡尔曼滤波算法应用于移动机器人定位系统时出现的误差值较大和算法发散现象,在定位算法中引入修正因子对状态估计方程进行优化。分析传统卡尔曼滤波和扩展卡尔曼滤波的定位算法原理,研究运动过程中驱动力和摩擦力对移动机器人的影响,引入修正因子改进卡尔曼滤波算法,并对传统卡尔曼滤波算法、扩展卡尔曼滤波算法和改进算法做仿真对比和研究。仿真结果表明:修正因子对传统卡尔曼滤波算法和扩展卡尔曼滤波算法都具有改进效果,能提高定位精度。

For the error value and divergence problem in the application of traditional Kalman filtering algorithm and extended Kalman filtering algorithm in mobile robot positioning system, the modification factor was introduced into the localization algorithm to optimize the state estimation equation. The positioning algorithm theories of traditional Kalman filtering and extended Kalman filtering were analyzed, and the influence of driving force and friction force on mobile robot was researched. Finally the modification factor was introduced to improve the Kalman filter algorithm, and the traditional Kalman filter algorithm, extended Kalman filtering algorithm and improved algorithm were compared by simulation results. The simulation results show that modification factor improves the classical Kalman filtering algorithm and the extended Kalman filter algorithm and it also improves the positioning accuracy.