基于TSOA定位原理混合算法的掘进机位姿检测方法
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摘要
为实现掘进机的位姿检测,提出了一种面向掘进机的混合算法的位姿检测方法。基于超宽带(Ultra-wideband)测距和(Time Summation of Arrival,TSOA)定位原理,系统地推导了混合算法的计算过程,将间接法得到的定位点初始坐标代入Taylor级数展开法,循环迭代,通过混合算法得到了掘进机的坐标值,将坐标值代入姿态角解算公式得到了掘进机的姿态角。实际实验中需要其他测量方式的标定才可验证真值,且任何测量方式均有误差,因此基于MATLAB进行仿真分析研究规律。绘制了混合算法程序流程图,仿真对比了间接法和混合算法在15 m和90 m的定位点空间分布状态、三轴误差,以及2种算法在10～100 m的测量范围内定位点均方根误差和三轴均方根误差变化情况,分析了混合算法在15 m和90 m的姿态角精度,仿真结果表明:混合算法的定位性能优于间接法,精度高于间接法,姿态角误差可控制在0. 008°以下。搭建了掘进机位姿检测系统实验平台,在模拟巷道中进行了实验验证,完整地采集了UWB测距的相关数据,在MATLAB中通过曲线拟合绘制了误差随距离变化的曲线图,得到了误差随测量距离变化的规律,实验结果表明:在3～94 m测量范围内,X轴误差可控制在4 cm以内,Y轴误差可达到毫米级,Z轴误差随测量距离增大而增大。为实现综掘装备的自主导控提供了理论基础。
In order to realize the pose detection of the roadheader, a pose detection method for the hybrid algorithm was proposed. According to the positioning principle of Time Summation of Arrival(TSOA),the calculation process of the hybrid algorithm was systematically derived. The initial coordinates of the positioning point obtained by the indirect method were brought into the Taylor series expansion, loop iteration, the coordinate values of the roadheader were obtained by the hybrid algorithm. Taking the coordinate value into the attitude angle solution formula,the attitude angle of the roadheader was obtained. The actual measurement required the calibration of other measurement methods to verify the true value. Any measurement method had errors, so the simulation analysis was carried out in MATLAB. The flow chart of the hybrid algorithm program was drawn. The simulation compared the spatial distribution and triaxial error of both indirect and hybrid algorithms at 15 m and 90 m. It compared the RMSE error of the positioning point and the three-axis RMSE error of the two algorithms in 10-100 m. The attitude angle accuracy of the hybrid algorithm was analyzed at 15 m and 90 m. The results show that the positioning performance of the hybrid algorithm is better than the indirect method. Hybrid algorithm is more accurate than indirect method. The attitude angle error can be controlled below 0. 008°. The experimental platform of the roadheader pose detection system was built. Experiments were carried out in the simulated roadway,and the relevant data of ultra-wideband ranging was collected completely. The curve of error with distance was plotted by curve fitting in MATLAB. The law of error with measurement distance was obtained. The experiment results show that the X-axis error can be controlled within 4 cm and the Y-axis error can reach millimeter,Z-axis error increases as the measured distance increases within 3-94 m. This method provides a basis for the realization of the position detection of roadheader.
In order to realize the pose detection of the roadheader, a pose detection method for the hybrid algorithm was proposed. According to the positioning principle of Time Summation of Arrival(TSOA),the calculation process of the hybrid algorithm was systematically derived. The initial coordinates of the positioning point obtained by the indirect method were brought into the Taylor series expansion, loop iteration, the coordinate values of the roadheader were obtained by the hybrid algorithm. Taking the coordinate value into the attitude angle solution formula,the attitude angle of the roadheader was obtained. The actual measurement required the calibration of other measurement methods to verify the true value. Any measurement method had errors, so the simulation analysis was carried out in MATLAB. The flow chart of the hybrid algorithm program was drawn. The simulation compared the spatial distribution and triaxial error of both indirect and hybrid algorithms at 15 m and 90 m. It compared the RMSE error of the positioning point and the three-axis RMSE error of the two algorithms in 10-100 m. The attitude angle accuracy of the hybrid algorithm was analyzed at 15 m and 90 m. The results show that the positioning performance of the hybrid algorithm is better than the indirect method. Hybrid algorithm is more accurate than indirect method. The attitude angle error can be controlled below 0. 008°. The experimental platform of the roadheader pose detection system was built. Experiments were carried out in the simulated roadway,and the relevant data of ultra-wideband ranging was collected completely. The curve of error with distance was plotted by curve fitting in MATLAB. The law of error with measurement distance was obtained. The experiment results show that the X-axis error can be controlled within 4 cm and the Y-axis error can reach millimeter,Z-axis error increases as the measured distance increases within 3-94 m. This method provides a basis for the realization of the position detection of roadheader.
引文
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[15] ZHANG Weiqiang,ZHONG Zhimin,KANG Shaoguang. Microstructures and mechanical properties of a martensitic steel welded with flux-cored wires[J]. International Journal of Coal Science&Technology,2015,2(3):254-260.
[16]符世琛,李一鸣,宗凯,等.面向掘进机的超宽带位姿检测系统精度分析[J].仪器仪表学报,2017,38(8):1978-1987.FU Shichen, LI Yiming, ZONG Kai,et al. Accuracy analysis of UWB pose detection system for roadheader[J]. Chinese Journal of Scientific Instrument,2017,38(8):1978-1987.
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[18]符世琛,成龙,陈慎金,等.面向掘进机的超宽带位姿协同检测方法[J].煤炭学报,2018,43(10):2918-2925.FU Shichen, CHENG Long, CHEN Shenjin,et al. Ultra-wideband pose collaborative detection method of roadheader[J]. Journal of China Coal Society,2018,43(10):2918-2925.
[19]田孝华,周义建.无线电定位理论与技术[M].北京:国防工业出版社,2011.
[20]赵红梅,赵杰磊.超宽带室内定位算法综述[J].电信科学,2018,34(9):130-142.ZHAO Hongmei,ZHAO Jielei. An overview of ultra-wideband indoor positioning algorithms[J]. Telecommunications Science,2018,34(9):130-142.
[21]符世琛,李一鸣,张敏骏,等.基于UWB信号的TW-TOF测距技术在狭长巷道中的精度测试实验研究[J].煤炭技术,2017,36(3):246-248.FU Shichen,LI Yiming,ZHANG Minjun,et al. Accuracy testing experiment in narrow roadway based on TW-TOF ranging technique of UWB signals[J]. Coal Technology,2017,36(3):246-248.
[22] FU Shichen, LI Yiming, ZHANG Minjun,et al. Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion[J]. Measurement Science&Technology,2018,29(1).
[23]范平志,邓平,刘林,等.蜂窝网无线定位[M].北京:电子工业出版社,2012.
[2]葛世荣.智能化采煤装备的关键技术[J].煤炭科学技术,2014,42(9):7-11.GE Shirong. Key technology of intelligent coal mining equipment[J]. Coal Science and Technology,2012,42(9):7-11.
[3]彭信山.综掘工作面复杂条件下人一环境耦合关系研究[D].焦作:河南理工大学,2011.PENG Xinshan. Study on coupling relation of man-environment under complicated conditions of fully mechanized excavation face[D].Jiaozuo:Henan Polytechnic University,2011.
[4]王虹.我国综合机械化掘进技术发展40 a[J].煤炭学报,2010,35(11):1815-1820.WANG Hong. The 40 years developmental review of the fully mecha-nizedmine roadway heading technology in China[J]. Journal of China Coal Society,2010,35(11):1815-1820.
[5]李惠平,夏明耀.盾构姿态自动控制技术的应用与发展[J].地下空间,2003(1):75-78,109.LI Huiping,XIA Mingyao. Application and development of shield attitude automatic control technology[J]. Underground Space,2003(1):75-78,109.
[6]梅宁.艾柯夫ET480隧道掘进机[J].建井技术,2008(3):42.MEI Ning. Ekoff ET480 tunnel boring machine[J]. Mine Construction Technology,2008(3):42.
[7]陶云飞.掘进机位姿激光自动测量方法及系统研究[D].北京:中国矿业大学(北京),2017.TAO Yunfei. Research on automatic measurement method and system of position and attitude of roadheader based on laser measurement system[D]. Beijing:China University of Mining and Technology(Beijing),2017.
[8]周玲玲,董海波,杜雨馨.基于双激光标靶图像识别的掘进机位姿检测方法[J].激光与光电子学进展,2017,54(4):186-192.ZHOU Lingling,DONG Haibo,DU Yuxin. Method of roadheader position detection based on image recognition of double laser targets[J]. Laser&Optoelectronics Progress,2017,54(4):186-192.
[9]童敏明,杜雨馨,李高军,等.多传感器的掘进机定位系统研究[J].煤矿机械,2013,34(6):146-148.TONG Minming,DU Yuxin,LI Gaojun,etc al. Study on positioning system of roadheader by multi sensor[J]. Coal Mine Machinery,2013,34(6):146-148.
[10]杜雨馨,刘停,童敏明,等.基于机器视觉的悬臂式掘进机机身位姿检测系统[J].煤炭学报,2016,41(11):2897-2906.DU Yuxin,LIU Ting,TONG Minming,et al. Pose measurement system of boom-type roadheader based on machine vision[J]. Journal of China Coal Society,2016,41(11):2897-2906.
[11]贾文浩,陶云飞,张敏骏,等.基于iGPS的煤巷狭长空间中掘进机绝对定位精度研究[J].仪器仪表学报,2016,37(8):1920-1926.JIA Wenhao,TAO Yunfei,ZHANG Minjun,et al. Research on absolute positioning accuracy of roadheader based on indoor global positioning system in narrow and long coal tunnel[J]. Chinese Journal of Scientific Instrument,2016,37(8):1920-1926.
[12]吴淼,贾文浩,华伟,等.基于空间交汇测量技术的悬臂式掘进机位姿自主测量方法[J].煤炭学报,2015,40(11):2596-2602.WU Miao,JIA Wenhao,HUA Wei,et al. Autonomous measurement of position and attitude of boom-type roadheader based on space intersection measurement[J]. Journal of China Coal Society,2015,40(11):2596-2602.
[13]黄东,杨凌辉,罗文,等.基于视觉/惯导的掘进机实时位姿测量方法研究[J].激光技术,2017,41(1):19-23.HUANG Dong, YANG Linghui,LUO Wen,et al. Study on measurement method of real time position and attitude of roadheader based on vision/inertial navigation system[J]. Laser Technology,2017,41(1):19-23.
[14]田原.悬臂式掘进机导航技术现状及其发展方向[J].工矿自动化,2017,43(8):37-43.TIAN Yuan. Present situation and development direction of navigation technology of boom-type roadheader[J]. Industry and Mine Automation,2017,43(8):37-43.
[15] ZHANG Weiqiang,ZHONG Zhimin,KANG Shaoguang. Microstructures and mechanical properties of a martensitic steel welded with flux-cored wires[J]. International Journal of Coal Science&Technology,2015,2(3):254-260.
[16]符世琛,李一鸣,宗凯,等.面向掘进机的超宽带位姿检测系统精度分析[J].仪器仪表学报,2017,38(8):1978-1987.FU Shichen, LI Yiming, ZONG Kai,et al. Accuracy analysis of UWB pose detection system for roadheader[J]. Chinese Journal of Scientific Instrument,2017,38(8):1978-1987.
[17]山德,达曼,门兴,等.无线通信系统中的定位技术与应用[M].北京:机械工业出版社,2015.
[18]符世琛,成龙,陈慎金,等.面向掘进机的超宽带位姿协同检测方法[J].煤炭学报,2018,43(10):2918-2925.FU Shichen, CHENG Long, CHEN Shenjin,et al. Ultra-wideband pose collaborative detection method of roadheader[J]. Journal of China Coal Society,2018,43(10):2918-2925.
[19]田孝华,周义建.无线电定位理论与技术[M].北京:国防工业出版社,2011.
[20]赵红梅,赵杰磊.超宽带室内定位算法综述[J].电信科学,2018,34(9):130-142.ZHAO Hongmei,ZHAO Jielei. An overview of ultra-wideband indoor positioning algorithms[J]. Telecommunications Science,2018,34(9):130-142.
[21]符世琛,李一鸣,张敏骏,等.基于UWB信号的TW-TOF测距技术在狭长巷道中的精度测试实验研究[J].煤炭技术,2017,36(3):246-248.FU Shichen,LI Yiming,ZHANG Minjun,et al. Accuracy testing experiment in narrow roadway based on TW-TOF ranging technique of UWB signals[J]. Coal Technology,2017,36(3):246-248.
[22] FU Shichen, LI Yiming, ZHANG Minjun,et al. Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion[J]. Measurement Science&Technology,2018,29(1).
[23]范平志,邓平,刘林,等.蜂窝网无线定位[M].北京:电子工业出版社,2012.