Positioning method of an orbital inspection robot for belt conveyors based on encoder and NFC correction fusion
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摘要: 軌道式巡檢機器人的高精度定位技術是帶式輸送機智能化巡檢的重要研究方向之一，而礦用帶式輸送機距離超長，工作環境復雜，嚴重影響巡檢機器人的定位精度。針對目前的軌道式巡檢機器人定位技術在礦用帶式輸送機巡檢領域存在的問題，提出了基于編碼器和NFC雙傳感器修正融合的高精度定位方法。分析帶式輸送機軌道式巡檢機器人軌道與環境特性對編碼器系數的影響，提出軌道分段原則。利用機器人搭載的編碼器數據反饋特點，構建編碼器遞推定位方法。通過機器人運行的歷史數據，對編碼器系數進行分段分方向修正，并提出基于遞推最小二乘的編碼器系數修正方法，以提高編碼器對軌道環境的適應性。在此基礎上，根據機器人所在軌道分段的位置不同，在段端基于卡爾曼濾波算法實現編碼器和NFC數據融合，在段內利用分段分方向修正系數與編碼器信息進行遞推定位，實現軌道式巡檢機器人連續高精度的定位。針對所提方法搭建了實驗平臺并進行了實物測試，實驗結果表明，相較于編碼器定位、RFID定位和兩者融合定位三種傳統定位方式，基于編碼器和NFC的修正融合定位算法能夠有效提高軌道式巡檢機器人定位對軌道環境的適應性，同時提高軌道式巡檢機器人的定位精度。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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圖 3 基于編碼器和NFC修正融合的定位算法流程
Figure 3. Localization algorithm of corrective fusion based on encoder and NFC
圖 9 四種算法第10次正向定位效果
Figure 9. Effect of the 10th forward positioning of the four algorithms
圖 10 三種算法第10次正向誤差對比
Figure 10. Comparison of the 10th forward errors of the three algorithms
表 1 定位傳感器參數
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 參數取值
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 三種算法第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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