基于UWB的无人运输车的导航定位算法研究

Research on navigation and positioning algorithm for unmanned vehicle based on UWB

针对室内无人运输车的自主导航与定位提出一种全新的基于UWB信号的改进算法。该算法首先根据测距数值的标准差这一统计特征进行非视距误差的处理,再根据当前的运动特征(终点、横方向、纵方向速度等)与观测值进行改进的扩展卡尔曼滤波定位估计,创新性地提出了每次定位估计之后,更新状态的同时更新下一时刻运动特征(即更新预测依据)的思想,有效减小了定位误差,使定位精度达到厘米级,同时增强了算法的抗干扰性。

Aiming at the autonomous navigation and localization of the indoor unmanned vehicle,an improved algorithm based on UWB signal is proposed in this paper. Firstly,the distance measurement is processed,and the non-sight-distance error is processed according to the standard deviation of the range value. Then the extended Kalman filter algorithm is used to locate on the basis of the present motion features and observed value. The idea of updating the next-time motion feature is proposed innovatively,that is updating the prediction criteria when the present state is updated after each location calculation. This algorithm can effectively reduce the positioning error and make the precision reach centimeter level. At the same time,the anti-interference ability of the algorithm is enhanced.