基于UWB的物流仓储自动导引车的定位系统研究
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摘要
针对传统物流仓储环境下自动导引车定位精度低的问题,提出基于UWB的物流仓储自动导引车定位系统。该定位系统采用改进增量卡尔曼滤波算法对采集到的距离值进行滤波,避免了受非视距(NLOS)及环境、测量设备等引起的误差;与Taylor级数法对比,采用更适合的Chan算法进行解算获得目标位置;设计编写了基于MATLAB串口通信的上位机程序,实现了实时位置的可视化显示。仿真试验结果表明,该系统定位精度显著提高,上位机界面为仓储监管提供了信息支撑。
In order to solve the problem of low positioning precision in traditional logistics storage environment, a UWB positioning system of automatic guidance vehicle for logistics warehousing is proposed. The location system uses the improved incremental Kalman filter algorithm to filter the distance values collected by UWB, which avoids the errors caused by NLOS,measuring equipment and environment; Chan algorithm is adopted, which is more suitable to calculate the positioning result compared with Taylor series method; The host computer program based on MATLAB is designed, which supports the serial communication with automatic guidance vehicle and realizes the visual display of its position in real time. The simulation results show that the proposed system is obviously improved in positioning precision, and the host computer interface provides the information support for warehousing supervision.
In order to solve the problem of low positioning precision in traditional logistics storage environment, a UWB positioning system of automatic guidance vehicle for logistics warehousing is proposed. The location system uses the improved incremental Kalman filter algorithm to filter the distance values collected by UWB, which avoids the errors caused by NLOS,measuring equipment and environment; Chan algorithm is adopted, which is more suitable to calculate the positioning result compared with Taylor series method; The host computer program based on MATLAB is designed, which supports the serial communication with automatic guidance vehicle and realizes the visual display of its position in real time. The simulation results show that the proposed system is obviously improved in positioning precision, and the host computer interface provides the information support for warehousing supervision.
引文
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[11]Shuai He,Xiao-dai Dong,Wu-Sheng Lu.Localization algorithms for asynchronous time difference of arriva positioning systems[J].EURASIP Journal on Wireless Communications and Networking,2017.1:1
[2]朱士光,张帆,张聪.基于惯导和视觉定位的AGV仓储机器人[J].中国新技术新产品,2018.3:21-22
[3]刘丽军.基于单目视觉的仓储物流机器人定位方法探讨[J].电子制作,2018.12:63-64
[4]张涛,马磊,梅玲玉.基于单目视觉的仓储物流机器人定位方法[J].计算机应用,2017.37(9):2491-2495
[5]王绍丹,王宜怀,刘锴.基于射频识别和无线传感网融合技术的仓储定位方法研究[J].计算机应用研究,2018.35(1):195-198
[6]徐英凯,曹浩,陈晓群.一种抑制非视距传播误差的混合定位算法[J].计算机工程,2013.39(7):119-122
[7]He Jie,Geng Yi-shuang,Liu Fei,Xu Cheng.CC-KF:Enhanced TOA performance in multipath and NLOSindoor extreme environment[J].IEEE Sensors Journal,2014.14(11):3766-3774
[8]张恒.基于UWB的室内高精度定位方法研究与应用[D].辽宁工程技术大学,2015.
[9]傅惠民,吴云章,娄泰山.欠观测条件下的增量Kalman滤波方法[J].机械强度,2012.34(1):43-47
[10]蔡博.基于UWB的AGV高精度室内定位系统研究[D].西南交通大学,2018.
[11]Shuai He,Xiao-dai Dong,Wu-Sheng Lu.Localization algorithms for asynchronous time difference of arriva positioning systems[J].EURASIP Journal on Wireless Communications and Networking,2017.1:1