Research on precise positioning of shield roadheader robot system in coal mine
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摘要: 目前煤矿井下掘进装备定位方法大多采用机器视觉、里程计、全站仪等单一的辅助测量方式与惯导组合测量来抑制惯导解算随时间所产生的位置累计误差，但是单一的辅助测量方式易受井下环境的影响，位置测量存在一定的误差，从而导致与惯导组合测量方法的精度降低。针对上述问题，以煤矿护盾式掘进机器人系统为研究对象，提出了一种捷联惯导+数字全站仪+位移传感器的组合定位方法。首先采用捷联惯导解算出掘进机器人的位置与姿态角参数；然后利用数字全站仪测量的掘进机器人位置信息与位移传感器推算的掘进机器人位置信息对捷联惯导解算出的位置信息进行反馈修正，以减小惯导随时间所产生的位置累计误差；最后利用基于联邦滤波器的多信息融合算法将捷联惯导解算出的位置及姿态角信息、全站仪测量得到的位置信息及位移传感器推算得到的位置信息进行融合，从而得到掘进机器人准确的位姿信息。仿真及工业性试验结果表明：该组合定位方法能够很好地抑制纯惯导位置解算误差累计，实现煤矿护盾式掘进机器人的精确定位，x轴和y轴方向上的位置误差分别控制在±0.03 m和±0.02 m，满足井下掘进工作面要求。Abstract: At present, most of the positioning methods of underground tunneling equipment in coal mines adopt single auxiliary measurement methods such as machine vision, odometer and total station to combine with inertial navigation measurement to suppress the cumulative position error caused by inertial navigation solution over time. However, the single auxiliary measurement method is easy to be affected by the underground environment, and there are certain errors in position measurement, which leads to the reduction of the precision of the combined measurement method with inertial navigation. In order to solve the above problems, taking shield roadheader robot system in coal mine as the research object, a combined positioning method of strapdown inertial navigation+digital total station+displacement sensor is proposed. Firstly, the position and attitude angle parameters of the roadheader robot are calculated by using strapdown inertial navigation. Secondly, the position information of the roadheader robot measured by the digital total station and calculated by the displacement sensor are used to feedback and correct the position information calculated by the strapdown inertial navigation, so as to reduce the cumulative position error generated by the inertial navigation over time. Finally, the position and attitude angle information calculated by the strapdown inertial navigation, the position information obtained by the total station measurement and the position information estimated by the displacement sensor are fused by the multi-information fusion algorithm based on the federated filter, so as to obtain the accurate position and attitude information of the roadheader robot. The simulation and industrial experiment results show that the combined positioning method can well suppress the accumulative position solution errors of pure inertial navigation and realize the precise positioning of the shield roadheader robot in coal mines. The position errors in the x-axis and y-axis directions are controlled at ±0.03 m and ±0.02 m respectively, which meets the requirements of the underground driving face.
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图 2 组合定位方法组成
1−后视棱镜；2−数字全站仪；3−临时支护机器人Ⅱ；4−推移油缸；5−捷联惯导；6−前视棱镜；7−临时支护机器人Ⅰ；8−截割机器人；9−横滚角；10−航向角；11−俯仰角。
Figure 2. Composition of combination positioning method
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图 4 基于联邦滤波器的多信息融合原理
Figure 4. Multi-information fusion principle based on federated filter
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图 5 组合定位方法下掘进机器人静态姿态角误差
Figure 5. Static attitude angle error of roadheader robot under combined positioning method
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图 6 组合定位方法下掘进机器人动态姿态角误差
Figure 6. Dynamic attitude angle error of roadheader robot under combined positioning method
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图 7 组合定位方法下掘进机器人位置解算误差
Figure 7. Position calculation error of roadheader robot under combined positioning method
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图 8 纯惯导定位位置解算误差
Figure 8. Position calculation error of pure inertial navigation positioning
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图 11 组合定位方法下煤矿护盾式掘进机器人位置曲线
Figure 11. Position curves of coal mine shield roadheader robot under combined positioning method
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