Trajectory determination for pipelines using an inspection robot and pipeline features

• Autorzy: Zhang Shuo , Dubljevic Stevan

Identyfikatory

DOI

10.24425/mms.2021.137134

• Języki publikacji: EN

Abstrakty

EN

Geographic trajectory of a pipeline is important information for pipeline maintenance and leak detection. Although accurate trajectory of a ground pipeline usually can be directly measured by using global positioning system technology, it is much difficult to determine trajectory for an underground pipeline where global positioning system signal cannot be received. In this paper, a new method to determine trajectory for an underground pipeline by using a pipeline inspection robot is proposed. The robot is equipped with a low-cost inertial measurement unit and odometers. The kinematic model, measurement model and error propagation model are established for estimating position, velocity and attitude of the robot. The path reconstruction algorithm for the robot is proposed to improve accuracy of trajectory determination based on pipeline features. The experiment is given to illustrate that the position errors of the proposed method are less than 40% of that of the standard extended Kalman filter.

Słowa kluczowe

EN

trajectory determination; pipeline inspection robot; pipeline feature; path reconstruction algorithm

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2021
• Tom: Vol. 28, nr 3
• Strony: 439--453
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr., wzory

Twórcy

autor

Zhang Shuo

• University of Alberta, Department of Chemical & Materials Engineering, T6G 2R3 Edmonton, AB, Canada

autor

Dubljevic Stevan

• University of Alberta, Department of Chemical & Materials Engineering, T6G 2R3 Edmonton, AB, Canada

Bibliografia

• [1] Liu, Z., & Kleiner, Y. (2013). State of the art review of the inspection technologies for condition assessment of water pipes. Measurement, 46(1), 1-15. https://doi.org/10.1016/j.measurement.2012.05.032
• [2] Kishawy, H. A., & Gabbar, H. A. (2010). Review of pipeline integrity management practices. International Journal of Pressure Vessels and Piping, 87(7), 373-380. https://doi.org/10.1016/https://j.ijpvp.2010.04.003
• [3] Zhang, T., Wang, X., Chen, Y., Shuai, Y., Ullah, Z., Ju, H., & Zhao, Y. (2019). Geomagnetic detection method for pipeline defects based on ceemdan and WEP-TEO. Metrology and Measurement Systems, 26(2), 345-361. https://doi.org/10.24425/mms.2019.128363
• [4] Ju, H., Wang, X., Zhang, T., Zhao, Y., & Ullah, Z. (2019). Defect recognition of buried pipeline based on approximate entropy and variational mode decomposition. Metrology and Measurement Systems, 26(4), 735-755. https://doi.org/10.24425/mms.2019.129587
• [5] Piao, G., Guo, J., Hu, T., & Deng, Y. (2019). High-sensitivity real-time tracking system for high-speed pipeline inspection gauge. Sensors, 19(3), 731. https://doi.org/10.3390/s19030731
• [6] De Araújo, R. P., De Freitas, V. C. G., De Lima, G. F., Salazar, A. O., Neto, A. D. D., & Maitelli, A. L. (2018). Pipeline inspection gauge’s velocity simulation based on pressure differential using artificial neural networks. Sensors, 18(9), 3072. https://doi.org/10.3390/s18093072
• [7] Chowdhury, M. S., & Abdel-Hafez, M. F. (2016). Pipeline inspection gauge position estimation using inertial measurement unit, odometer, and a set of reference stations. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, 2(2), 021001-1-10. https://doi.org/10.1115/1.4030945
• [8] Coramik, M., & Ege, Y. (2017). Discontinuity inspection in pipelines: a comparison review. Measurement, 111, 359-373. https://doi.org/10.1016/j.measurement.2017.07.058
• [9] Idroas, M., Abd Aziz, M. F. A., Zakaria, Z., & Ibrahim, M. N. (2019). Imaging of pipeline irregularities using a PIG system based on reflection mode ultrasonic sensors. International Journal of Oil, Gas and Coal Technology, 20(2), 212-223. https://doi.org/10.1504/IJOGCT.2019.097449
• [10] Li, Z., Wang, J., Li, B., & Gao, J. (2014). GPS/INS/Odometer integrated system using fuzzy neural network for land vehicle navigation. Journal of Navigation, 67(6), 967-983. https://doi.org/10.1017/S0373463314000307
• [11] Jiang, Q., Wu, W., Jiang, M., & Li, Y. (2017). A new filtering and smoothing algorithm for railway track surveying based on landmark and IMU/odometer. Sensors, 17(6), 1438. https://doi.org/10.3390/s17061438
• [12] Georgy, J., Karamat, T., Iqbal, U., & Noureldin, A. (2011). Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter. GPS Solutions, 15(3), 239-252. https://doi.org/10.1007/s10291-010-0186-4
• [13] Zhao, Y. (2015) Cubature plus extended hybrid Kalman filtering method and its application in PPP/IMU tightly coupled navigation systems. IEEE Sensors Journal, 15(12), 6973-6985. https://doi.org/10.1109/JSEN.2015.2469105
• [14] Guan, L., Cong, X., Zhang, Q., Liu, F., Gao, Y., An, W., & Noureldin, A. (2020). A comprehensive review of micro-inertial measurement unit based intelligent PIG multi-sensor fusion technologies for small-diameter pipeline surveying. Micromachines, 11(9), 840. https://doi.org/10.3390/mi11090840
• [15] Wang, L., Wang, W., Zhang, Q., & Gao, P. (2014). Self-calibration method based on navigation in high-precision inertial navigation system with fiber optic gyro. Optical Engineering, 53(6), 064103. https://doi.org/10.1117/1.OE.53.6.064103
• [16] Usarek, Z., & Warnke, K. (2017). Inspection of gas pipelines using magnetic flux leakage technology. Advances in Materials Science, 17(3), 37-45. https://doi.org/10.1515/adms-2017-0014
• [17] Sasani, S., Asgari, J., & Amiri-Simkooei, A. R. (2016). Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications. GPS solutions, 20(1), 89-100. https://doi.org/10.1007/s10291-015-0471-3
• [18] Hyun, D., Yang, H. S., Park, H. S., & Kim, H. J. (2010). Dead-reckoning sensor system and tracking algorithm for 3-D pipeline mapping. Mechatronics, 20(2), 213-223. https://doi.org/10.1016/j.mechatronics.2009.11.009
• [19] Lee, D. H., Moon, H., Koo, J. C., & Choi, H. R. (2013). Map building method for urban gas pipelines based on landmark detection. International Journal of Control, Automation, and Systems, 11(1), 127-135. https://doi.org/10.1007/s12555-012-0049-6
• [20] Li, T., Zhang, H., Niu, X., & Gao, Z. (2017). Tightly-coupled integration of multi-GNSS single-frequency RTK and MEMS-IMU for enhanced positioning performance. Sensors, 17(11), 2462. https://doi.org/10.3390/s17112462
• [21] Sahli, H., & El-Sheimy, N. (2016). A novel method to enhance pipeline trajectory determination using pipeline junctions. Sensors, 16(4), 567. https://doi.org/10.3390/s16040567
• [22] Guan, L., Cong, X., Sun, Y., Gao, Y., Iqbal, U., & Noureldin, A. (2017). Enhanced MEMS SINS aided pipeline surveying system by pipeline junction detection in small diameter pipeline, IFAC-PapersOnLine, 50(1), 3560-3565. https://doi.org/10.1016/j.ifacol.2017.08.962
• [23] Crassidis, J. L., & Junkins, J. L. (2011). Optimal Estimation of Dynamic Systems. CRC press. https://doi.org/10.1201/b11154
• [24] Noureldin, A., Karamat, T. B., & Georgy, J. (2012). Fundamentals of Inertial Navigation, Satellite-Based Positioning and their Integration. Springer Science & Business Media. https://doi.org/10.1007/978-3-642-30466-8
• [25] Xu, L., Li, X. R., Duan, Z., & Lan, J. (2013). Modeling and state estimation for dynamic systems with linear equality constraints. IEEE Transactions on Signal Processing, 61(11), 2927-939. https://doi.org/10.1109/TSP.2013.2255045

Uwagi

1. This work was supported by the Canada Mitacs Elevate Postdoctoral Fellowship Program (grant #IT15728).

2. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).