Research on tightly combined positioning method of coal mine robot based on UWB and IMU
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摘要: 针对煤矿井下环境复杂，现有煤矿机器人定位方法受非视距误差等因素影响导致定位精度低、实时性不高等问题，提出了一种基于UWB（超宽带）和IMU（惯性测量单元）的煤矿机器人紧组合定位方法。首先利用UWB模块测量煤矿机器人与UWB基站之间的距离，使用煤矿机器人与UWB基站之间的距离真实值和实测值训练最小二乘支持向量机（LSSVM）模型，得到LSSVM修正模型；然后将煤矿机器人定位过程中UWB模块测得的实测值作为LSSVM修正模型的输入，通过LSSVM修正模型对UWB实测值进行修正，减小非视距误差对定位精度的影响，得到较为准确的距离信息；最后将经过LSSVM修正模型修正后的测距信息作为误差状态卡尔曼滤波(ESKF)的量测输入，与惯性导航解算出的位置信息构成量测方程，使用ESKF对UWB测距修正值与惯性导航解算的距离信息紧组合，完成状态更新，得到更为精确的位置信息，实现煤矿机器人的精确定位。UWB基站不同布置方案下的模拟实验结果表明：使用LSSVM修正模型可使UWB测距信息更为准确，进而提高定位精度。静态定位实验时，当4个UWB基站等高对称布置时，定位的均方根误差由0.146 4 m减小到0.1398 m；当4个UWB基站不等高对称布置时，均方根误差由0.300 8 m减小到0.200 6 m；当4个基站无规律布置时，均方根误差由0.317 5 m减小到0.314 2 m。因此，在实际场景中，应尽可能使UWB基站等高对称布置。动态定位实验时，通过LSSVM修正模型对UWB测距信息进行修正后的融合定位轨迹相较于修正前的融合定位轨迹更接近煤矿机器人的真实轨迹，验证了该紧组合定位方法能够减小非视距误差，提高定位精度。Abstract: The underground coal mine environment is complex. The existing coal mine robot positioning methods have low positioning precision and low real-time performance caused by the non-line-of-sight (NLOS) error and other factors. In order to solve the above problems, a tightly combined positioning method of coal mine robot based on UWB (Ultra Wide Band) and IMU (Inertial Measurement Unit) is proposed. Firstly, the UWB module is used to measure distance between the coal mine robot and UWB base station. The least square support vector machine (LSSVM) model is trained by using real value and measured value of the distance between the coal mine robot and the UWB base station, and the modified LSSVM model is obtained. Secondly, the measured value measured by the UWB module during the positioning process of the coal mine robot is used as the input of the modified LSSVM model. The modified LSSVM model is used to correct the measured value of UWB, reduce the influence of NLOS error on positioning precision, and obtain more accurate distance information. Finally, the range information modified by modified LSSVM model is used as the measurement input of error-state Kalman filter (ESKF). The measurement equation is formed with the position information solved by inertial navigation. The ESKF is used to tightly combine the UWB ranging correction value with the range information calculated by the inertial navigation to complete the state update. The more precise position information of the coal mine robot is obtained, and the precise positioning of the coal mine robot is achieved. The simulation results under different layont schemes of UWB base stations show that using modified LSSVM model can make the UWB range information more accurate, and improve the positioning precision. In the static positioning experiment, when the four UWB base stations are symmetrically distributed at the same height, the root mean square error of the positioning is reduced from 0.146 4 m to 0.139 8 m. When the four UWB base stations are distributed symmetrically with unequal heights, the root mean square error decreases from 0.300 8 m to 0.200 6 m. When the four base stations are distributed irregularly, the root mean square error decreases from 0.317 5 m to 0.314 2 m. Therefore, in actual scenarios, the UWB base stations should be arranged symmetrically at the same height as possible. In the dynamic positioning experiment, the fusion positioning trajectory corrected by the modified LSSVM model is closer to the real trajectory of the coal mine robot than the fusion positioning trajectory before correction. The result verifies that the tightly combined positioning method can reduce the NLOS error and improve positioning precision.
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图 1 基于UWB和IMU的煤矿机器人紧组合定位方法原理
Figure 1. Principle of tightly combined positioning method of coal mine robot based on UWB and IMU
表 1 UWB基站位置坐标
Table 1. UWB base station layout coordinates
m 方案 位置坐标 基站0 基站1 基站2 基站3 方案1 (4.48,0,2) (−4.48,0,2) (4.15,8.06,2) (−4.15,8.06,2) 方案2 (4.48,0,2) (−4.48,0,2) (4.15,8.06,1) (−4.15,8.06,1) 方案3 (4.48,0,2) (−4.48,0,2) (4.59,7.80,1.44) (−4.5,6.4,1.2) 
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表 2 3种基站布置方案的实验均方根误差
Table 2. Experimental root mean square error of three base stations layout schemes
m 方案 原始误差 LSSVM模型修正后的误差 方案1 0.146 4 0.139 8 方案2 0.300 8 0.200 6 方案3 0.317 5 0.314 2
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[1] 柳玉龙. 煤矿搜救机器人的研究现状及关键技术分析[J]. 矿山机械,2013,41(3):7-12. doi: 10.16816/j.cnki.ksjx.2013.03.002LIU Yulong. Analysis on current research status and key technologies of mine search and rescue robots[J]. Mining & Processing Equipment,2013,41(3):7-12. doi: 10.16816/j.cnki.ksjx.2013.03.002 [2] 张晓莉,王张哲. 井下巡检机器人实时高精度定位方法[J]. 矿业研究与开发,2021,41(10):158-161. doi: 10.13827/j.cnki.kyyk.2021.10.027ZHANG Xiaoli,WANG Zhangzhe. Real-time and high-precision positioning method of underground patrol robot[J]. Mining Research and Development,2021,41(10):158-161. doi: 10.13827/j.cnki.kyyk.2021.10.027 [3] 谭玉新,杨维. 一种基于UKF的井下机器人超声网络定位方法[J]. 煤炭学报,2016,41(9):2396-2404. doi: 10.13225/j.cnki.jccs.2016.0075TAN Yuxin,YANG Wei. UKF-based ultrasonic network localization for a mine robot[J]. Journal of China Coal Society,2016,41(9):2396-2404. doi: 10.13225/j.cnki.jccs.2016.0075 [4] 陈美蓉,王凯,张嘉纯,等. 煤矿井下超宽带定位混合解算方法[J]. 工矿自动化,2021,47(3):53-59. doi: 10.13272/j.issn.1671-251x.17710CHEN Meirong,WANG Kai,ZHANG Jiachun,et al. Hybrid solution method for ultra-wideband positioning in coal mines[J]. Industry and Mine Automation,2021,47(3):53-59. doi: 10.13272/j.issn.1671-251x.17710 [5] 马宏伟,张璞,毛清华,等. 基于捷联惯导和里程计的井下机器人定位方法研究[J]. 工矿自动化,2019,45(4):35-42. doi: 10.13272/j.issn.1671-251x.2018100054MA Hongwei,ZHANG Pu,MAO Qinghua,et al. Research on positioning method of underground robot based on strapdown inertial navigation and odometer[J]. Industry and Mine Automation,2019,45(4):35-42. doi: 10.13272/j.issn.1671-251x.2018100054 [6] 杨金衡,宋单阳,田慕琴,等. 基于自适应卡尔曼滤波的双惯导采煤机定位方法[J]. 工矿自动化,2021,47(7):14-20,28. doi: 10.13272/j.issn.1671-251x.2021030113YANG Jinheng,SONG Danyang,TIAN Muqin,et al. Double inertial navigation shearer positioning method based on adaptive Kalman filter[J]. Industry and Mine Automation,2021,47(7):14-20,28. doi: 10.13272/j.issn.1671-251x.2021030113 [7] 贺磊,魏明生,仇欣宇,等. 基于UWB的井下人员定位算法研究[J]. 工矿自动化,2022,48(6):134-138.HE Lei,WEI Mingsheng,QIU Xinyu,et al. Research on positioning algorithm of underground personnel based on UWB[J]. Journal of Mine Automation,2022,48(6):134-138. [8] 火元亨. 基于惯性导航和UWB定位的室内无人车导航技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.HUO Yuanheng. Indoor unmanned vehicle navigation based on inertial navigation and UWB positioning[D]. Harbin: Harbin Institute of Technology, 2021. [9] 牛小骥,旷俭,陈起金. 采用MEMS惯导的小口径管道内检测定位方案可行性研究[J]. 传感技术学报,2016,29(1):40-44. doi: 10.3969/j.issn.1004-1699.2016.01.008NIU Xiaoji,KUANG Jian,CHEN Qijin. Study on the possibility of the PIG positioning using MEMS-based IMU[J]. Chinese Journal of Sensors and Actuators,2016,29(1):40-44. doi: 10.3969/j.issn.1004-1699.2016.01.008 [10] 张宝军,陈曦,廖延娜,等. 基于DL−LSTM的UWB/INS室内定位算法[J]. 传感器与微系统,2021,40(10):147-150. doi: 10.13873/J.1000-9787(2021)10-0147-04ZHANG Baojun,CHEN Xi,LIAO Yanna,et al. UWB/INS indoor positioning algorithm based on DL-LSTM[J]. Transducer and Microsystem Technologies,2021,40(10):147-150. doi: 10.13873/J.1000-9787(2021)10-0147-04 [11] XU Yuan,LI Yueyang,CHOON K A,et al. Seamless indoor pedestrian tracking by fusing INS and UWB measurements via LS-SVM assisted UFIR filter[J]. Neurocomputing,2020,388:301-308. DOI: 10.1016/j.neucom.2019.12.121. [12] 蒋来来. 基于LSSVM优化的电厂NO_x排放预测方法研究[D]. 保定: 华北电力大学, 2021.JIANG Lailai. Research on NO_x emission prediction method of power plant based on optimized LSSVM [D]. Baoding: North China Electric Power University, 2021. [13] 邹强,陆甫光,兰馗博,等. 基于ISRUKF的UWB/INS组合室内定位方法研究[J]. 天津大学学报(自然科学与工程技术版),2022,55(5):496-503.ZOU Qiang,LU Fuguang,LAN Kuibo,et al. UWB/INS combined indoor positioning method based on ISRUKF[J]. Journal of Tianjin University (Science and Technology),2022,55(5):496-503. [14] SHIN E H. Estimation techniques for low-cost inertial navigation[D]. Calgary: University of Calgary （Canada）, 2005. [15] 秦永元, 张洪钺, 汪叔华. 卡尔曼滤波与组合导航原理[M]. 西安: 西北工业大学出版社, 2015.QIN Yongyuan, ZHANG Hongyu, WANG Shuhua. Kalman filtering and combinatorial navigation principle[M]. Xi'an: Northwestern Polytechnical University Press, 2015. [16] FENG Daquan,WANG Chunqi,HE Chunlong,et al. Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation[J]. IEEE Internet of Things Journal,2020,7(4):3133-3146. doi: 10.1109/JIOT.2020.2965115 [17] 徐爱功,刘韬,隋心,等. UWB/INS紧组合的室内定位定姿方法[J]. 导航定位学报,2017,5(2):14-19. doi: 10.3969/j.issn.2095-4999.2017.02.003XU Aigong,LIU Tao,SUI Xin,et al. Indoor positioning and attitude determination method based on UWB/INS tightly coupled[J]. Journal of Navigation and Positioning,2017,5(2):14-19. doi: 10.3969/j.issn.2095-4999.2017.02.003 [18] 王川阳,王坚. 超宽带应急定位基站布设研究[J]. 测绘科学,2019,44(8):174-181. doi: 10.16251/j.cnki.1009-2307.2019.08.025WANG Chuanyang,WANG Jian. Study of base station layout of ultra wideband emergency positioning[J]. Science of Surveying and Mapping,2019,44(8):174-181. doi: 10.16251/j.cnki.1009-2307.2019.08.025 -
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