井下巷道内智能传感器移动轨迹研究

柏思忠

doi:10.13272/j.issn.1671-251x.2021080081

井下巷道内智能传感器移动轨迹研究

doi: 10.13272/j.issn.1671-251x.2021080081

柏思忠^1,2

1.
1. 中煤科工集团重庆研究院有限公司, 重庆 400039;
2.
2. 瓦斯灾害监控与应急技术国家重点实验室, 重庆 400039

基金项目:

国家重点研发计划资助项目（2018YFC0808303）；重庆市技术创新与应用发展重点项目（cstc2019jscx-mbdxX0007）；中煤科工集团重庆研究院有限公司项目（2020YBXM09）。

详细信息

作者简介:
柏思忠(1978-),男,四川阆中人,副研究员,硕士,主要研究方向为超声波流量检测、室内设备定位与导航、仪器仪表数据处理和传输,E-mail:bsz3188@163.com。

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-30
- 修回日期: 2022-02-12
- 网络出版日期: 2022-03-01

Research on moving trajectory of intelligent sensor in underground roadway

BAI Sizhong^1,2

1.
CCTEG Chongqing Research Institute, Chongqing 400039, China;
2.
State Key Laboratory of the Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400039, China

摘要

摘要: 针对现有组合惯性导航方式应用于井下巷道内智能传感器时失去完全自主优势和增加成本的问题，首先将井下巷道分解为多个一定垂高的二维平面，在二维平面形成多条既定路径，将智能传感器定位问题转换为既定路径上的移动轨迹跟踪问题；然后选用基于微机电系统的惯性测量单元（MEMS-based IMU）实现井下巷道内智能传感器惯性导航，结合零速修正和既定路径标定的方式反演移动轨迹。智能传感器在既定路径起始点零速修正后，整个既定路径上的移动分为直线惯性导航和交叉点标定2种模式。直线惯性导航：传感器航向角和横滚角变化值不超过阈值时，通过惯性导航组件解算得到传感器速度、位置和姿态角，计算出实时相对坐标和运动路径。交叉点标定：传感器运动到交叉点处时，根据交叉点已知坐标值对当前实时值进行校准，消除惯性导航的累计误差。在地面环形道路的试验结果表明：在未标定情况下，纯惯性导航的航向偏差约为30°，移动距离相对误差为5.5%；进行既定路径交叉点标定后，航向偏差约为2°，移动距离相对误差为0.8%，标定后移动轨迹和实际道路吻合程度更高。
- 智能传感器 /
- 移动轨迹 /
- 既定路径 /
- MEMS惯导 /
- 零速修正 /
- 交叉点标定
Abstract: In order to solve the problem that the existing integrated inertial navigation method loses completely autonomous advantage and increases cost when applied to intelligent sensors in underground roadway, the underground roadways are firstly decomposed into multiple two-dimensional planes with certain vertical height. And multiple established paths are formed in the two-dimensional planes, and the positioning problem of intelligent sensors is converted into the tracking problem of moving tracks on the established paths. Secondly, the inertial measurement unit based on the micro electro-mechanical system (MEMS-based IMU) is used to realize the inertial navigation of the intelligent sensor in the underground roadway, and the moving track is inverted by combining the zero velocity update and the established path calibration. After the zero velocity update of the intelligent sensor at the starting point of the established path, the moving of the whole established path can be divided into two modes, namely linear inertial navigation mode and intersection calibration mode. Linear inertial navigation: when the change values of the heading angle and the roll angle of the sensor do not exceed the threshold value, the speed, the position and the attitude angle of the sensor are calculated through the inertial navigation component, and the real-time relative coordinates and the motion path are calculated. Intersection calibration: when the sensor moves to the intersection, the current real-time value is calibrated according to the known coordinate value of the intersection so as to eliminate the accumulated error of inertial navigation. The results of the test on the ground ring road show that in the context of uncalibrated, the heading deviation of pure inertial navigation is about 30°, and the relative error of moving distance is 5.5%. After the calibration of the established path intersections, the heading deviation is about 2°, and the relative error of the moving distance is 0.8%. After calibration, the moving track is more consistent with the actual road.
- intelligent sensor /
- moving track /
- established path /
- MEMS inertial navigation /
- zero velocity update /
- intersection calibration

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