基于UWB的智能跟随车导航定位算法研究
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摘要
针对目前市场上现有智能跟随车定位精度不足,提出一种基于UWB信号的定位算法。在智能跟随车的上方安置两个固定基站,手持标签到两个基站的距离数据经过卡尔曼滤波算法的处理,利用三角函数进行计算,得出标签到两个基站中点的距离和偏移角度,将距离和角度数据传送给电机控制模块,通过PID控制算法调节PWM值,从而控制电机的转速和转向。实验表明,该方法能够实现标签定位的距离误差小于9 cm,角度误差小于10°,使智能跟随车的定位更为精准。
The accuracy of intelligent vehicle positioning in the current market cannot satisfy customers ′ needs. The positioning algorithm based on Ultra Wideband( UWB) was proposed in order to solve the problem. Two fixed anchors were placed on the intelligent positioning vehicle. The data of distance from the tag to the two anchors was caculated by Kalman filter algorithm. The trigonometric function was used to get the distance from the tag to the middle point of the two anchors and the offset angle. Then the distance and angle data were transmitted to the motor control module, which adjusted Pulse Width Modulation(PWM) value through Proportion Integration Differentiation( PID) algorithm to control the speed and directions of the motor. Experimental results show that the distance deviation is less than 9 cm and the angle deviation is less than 10 degrees by this method, which proves that intelligent vehicle positioning is more accurate than before.
The accuracy of intelligent vehicle positioning in the current market cannot satisfy customers ′ needs. The positioning algorithm based on Ultra Wideband( UWB) was proposed in order to solve the problem. Two fixed anchors were placed on the intelligent positioning vehicle. The data of distance from the tag to the two anchors was caculated by Kalman filter algorithm. The trigonometric function was used to get the distance from the tag to the middle point of the two anchors and the offset angle. Then the distance and angle data were transmitted to the motor control module, which adjusted Pulse Width Modulation(PWM) value through Proportion Integration Differentiation( PID) algorithm to control the speed and directions of the motor. Experimental results show that the distance deviation is less than 9 cm and the angle deviation is less than 10 degrees by this method, which proves that intelligent vehicle positioning is more accurate than before.
引文
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[6]贺晶晶,姜平,冯晓荣.基于UWB的无人运输车的导航定位算法研究[J].电子测量与仪器学报,2016,30(11):1743-1749.
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[9]IBRAHEEM M.Gyroscope-enhanced dead reckoning localization system for an intelligent Walker[C].2010 International Conference on Information Networking and Automation(ICINA).IEEE,2010:67-72.
[10]李俊杰,王世练,路军,等.基于TOA的中心车辆定位系统的算法研究[J].通信技术,2001(5):23-25.
[11]薛辉,王丛岭.基于单片机采用PID算法的电机运行控制系统设计[J].机床电器,2006,33(1):51-52.
[12]李炳宇,萧蕴诗,汪镭.PSO算法在工程优化问题中的应用[J].计算机工程与应用,2004,40(18):74-76.!!!!!!!!!!!!!!!!!!!!!!!!