Positioning method of an orbital inspection robot for belt conveyors based on encoder and NFC correction fusion
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摘要: 轨道式巡检机器人的高精度定位技术是带式输送机智能化巡检的重要研究方向之一，而矿用带式输送机距离超长，工作环境复杂，严重影响巡检机器人的定位精度。针对目前的轨道式巡检机器人定位技术在矿用带式输送机巡检领域存在的问题，提出了基于编码器和NFC双传感器修正融合的高精度定位方法。分析带式输送机轨道式巡检机器人轨道与环境特性对编码器系数的影响，提出轨道分段原则。利用机器人搭载的编码器数据反馈特点，构建编码器递推定位方法。通过机器人运行的历史数据，对编码器系数进行分段分方向修正，并提出基于递推最小二乘的编码器系数修正方法，以提高编码器对轨道环境的适应性。在此基础上，根据机器人所在轨道分段的位置不同，在段端基于卡尔曼滤波算法实现编码器和NFC数据融合，在段内利用分段分方向修正系数与编码器信息进行递推定位，实现轨道式巡检机器人连续高精度的定位。针对所提方法搭建了实验平台并进行了实物测试，实验结果表明，相较于编码器定位、RFID定位和两者融合定位三种传统定位方式，基于编码器和NFC的修正融合定位算法能够有效提高轨道式巡检机器人定位对轨道环境的适应性，同时提高轨道式巡检机器人的定位精度。Abstract: The high-precision positioning technology of a rail-type patrol robot is an important research direction in the area of intelligent patrol inspection of belt conveyors. An excessively long mining belt conveyor and a complex working environment severely affect the positioning accuracy of patrol robots. This study aims to address the problems of poor adaptability to tracks and limited positioning accuracy of the positioning technology of rail-type patrol robots in the field of mining belt conveyor patrol inspection. Therefore, a high-precision positioning method based on a modified fusion of encoder and near field communication, abbreviated NFC, double sensors is proposed. This work analyzes the influence of track and track environment characteristics of the belt conveyor track patrol robot on the encoder coefficient. It also proposes a track segmentation principle based on the same characteristics of a track surface, providing a basis for the subsequent correction and fusion algorithm. A recursive positioning method of the absolute value encoder is constructed based on the data feedback characteristics carried by the robot. Through the historical positioning sensor data of robot operation, the encoder coefficients are modified according to sections and directions. Further, the encoder coefficient correction method based on recursive least squares is proposed to improve the adaptability of the encoder to the track. Hence, corresponding positioning methods are constructed according to the different positions of the robot’s track segments. At the end of the segment, the fusion positioning of the encoder and NFC data are realized based on the Kalman filtering algorithm to reduce the cumulative error of the encoder. In the segment, to improve the positioning accuracy of the encoder, the subsection and direction correction coefficient and real-time data of the encoder are used for recursive positioning. Therefore, combined with the positioning of each section of the track, the continuous high-precision positioning of the track-type patrol robot on the entire track can be realized. Moreover, an experimental platform is built for the proposed method to conduct physical testing. The modified fusion positioning method is compared with encoder positioning, RFID positioning, and fusion positioning based on encoder and NFC. The results of the correction experiment indicate that the modified fusion localization algorithm based on the encoder and NFC can effectively improve the adaptability of orbital inspection robot localization to the orbital environment. Meanwhile, the results of the modified fusion experiment indicate that the positioning method can improve the positioning accuracy of the orbital inspection robot. Therefore, the proposed positioning method can be applied to the application scenario of a long-distance mining belt conveyor patrol inspection.
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Key words:
- belt conveyor /
- orbital inspection robot /
- parameter correction /
- information fusion /
- positioning
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图 3 基于编码器和NFC修正融合的定位算法流程
Figure 3. Localization algorithm of corrective fusion based on encoder and NFC
图 9 四种算法第10次正向定位效果
Figure 9. Effect of the10th forward positioning of the four algorithms
图 10 三种算法第10次正向误差对比
Figure 10. Comparison of the 10th forward errors of the three algorithms
表 1 定位传感器参数
Table 1. Positioning sensor parameters
Sensor Parameter Value Encoder Friction wheel diameter 63.5 mm Resolution 1024 (pulse per revolution) NFC Card read error 10 mm 
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表 2 参数取值
Table 2. Parameter value
Variable Value Variable Value ${\hat {\boldsymbol{k}}_j}\left( 0 \right) = k$ 0.1948 ${\boldsymbol{P}}\left( {0|0} \right)$ 1 ${{\boldsymbol{P}}_j}\left( 0 \right)$ $1.0 \times {10^{ - 7}}$ R 1 $\hat x\left( {0|0} \right)$ 0.1948 Q 1
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表 3 三种算法第10次正向精度
Table 3. Tenth forward accuracy of the three algorithms
Method Root mean square error of positioning/ mm Encoder positioning 72.7 Fusion positioning 22.1 Correct fusion positioning 16.6
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