Araştırma Makalesi
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Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion

Yıl 2021, Cilt: 25 Sayı: 5, 1102 - 1120, 30.10.2021

Abdullah Erhan Akkaya Muhammed Fatih Talu Ömür Aydoğmuş

https://doi.org/10.16984/saufenbilder.898072

Öz

Water is vital for all living beings, especially for a human. Automatic position detection of water leakage in water pipelines is very important to minimize the loss of labour, time, money spent on exploration and excavation in pipe inspection procedures. The main goal of detection is to prevent water loss. In this study, sensitive position detection, crack frequency band detection and external sphere studies of an in-pipe robot prototype have performed. During the precise position estimation, classical EKF, stationary region detection and location estimation using EHDE are performed with two different ANNs. In this way, online precise position estimation can be done on hardware that has not sufficient computational power for indoor robotic studies. In addition, the sound characteristics resulting from the crack at different hole size and water pressure intensity levels have investigated. Finally, a new sealing sphere design has devised and three different hydrophone sensor data have recorded on the SD card simultaneously. It has been found that the proposed ANN method has the performance to work online and can make a similar position estimation with the classical IMU position estimation method by 99%.

Anahtar Kelimeler

Sensor Fusion Artificial Neural Network Inertial Navigation Leak Detection

Destekleyen Kurum

TÜBİTAK

Proje Numarası

215E075

Teşekkür

This study was supported by the Scientific and Technological Research Council (TUBITAK) under 3501 - Career Development Program of Turkey, (Project Title: "Production of Medium-Scale Capsule Robot Prototype for Detection of Leakage in Water Pipes"; Project Number: 215E075).

Kaynakça

  • [1] R. McKenzie and C. Seago, “Assessment of real losses in potable water distribution systems: Some recent developments,” in Water Science and Technology: Water Supply, 2005, vol. 5, no. 1, pp. 33–40.
  • [2] P. Marin, B. Kingdom, and R. Liemberger, “The challenge of reducing non-revenue water (NRW) in developing countries - how the private sector can help : a look at performance-based service contracting,” Dec. 2006.
  • [3] S. M. Hooda, “Rajasthan Water Assessment : Potential for Private Sector Interventions,” New Delhi, © World Bank, 2017.
  • [4] “Infrastructure Report Card 2017.” [Online]. Available: https://www.infrastructurereportcard.org/ wp-content/uploads/2017/01/DrinkingWater-Final.pdf. [Accessed: 20-May2021].
  • [5] N. Pizzi and W. Lauer, Water distribution operator training handbook - Fourth Edition, 3rd ed. American Water Works Association, 2013.
  • [6] D. Misiunas, “Failure monitoring and asset condition assessment in water supply systems,” Sweden, 2005.
  • [7] O. Hunaidi, A. Wang, M. Bracken, T. Gambino, and C. Fricke, “Acoustic methods for locating leaks in municipal water pipe networks,” 2004, pp. 1–14.
  • [8] A.W.W.A., Water Audits and Loss Control Programs(M36): AWWA Manual of Practice, 4 edition. Denver: American Waterworks Association, 2016.
  • [9] S. H. Zyoud, L. G. Kaufmann, H. Shaheen, S. Samhan, and D. Fuchs-Hanusch, “A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS,” Expert Syst. Appl., vol. 61, pp. 86–105, Nov. 2016.
  • [10] S. Eggimann et al., “The Potential of Knowing More: A Review of Data-Driven Urban Water Management,” Environmental Science and Technology, vol. 51, no. 5. American Chemical Society, pp. 2538–2553, 07-Mar-2017.
  • [11] A. Gupta and K. D. Kulat, “A Selective Literature Review on Leak Management Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion Techniques for Water Distribution System,” Water Resources Management, vol. 32, no. 10. Springer Netherlands, pp. 3247–3269, 01-Aug-2018.
  • [12] M. FIRAT, S. YILMAZ, and C. ORHAN, “Su kayıp yönetimi için temel hesaplama araçlarının geliştirilmesi ve temel su kayıp bileşenlerinin analizi,” Gümüşhane Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 11, no. 2, pp. 405–416, Feb. 2021.
  • [13] M. Eugine, “Predictive Leakage Estimation using the Cumulative Minimum Night Flow Approach,” Am. J. Water Resour., vol. 5, no. 1, pp. 1–4, Jan. 2017.
  • [14] E. Farah and I. Shahrour, “Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow,” Water Resour. Manag., vol. 31, no. 15, pp. 4821– 4833, Dec. 2017.
  • [15] E. Roshani and Y. Filion, “WDS leakage management through pressure control and pipes rehabilitation using an optimization approach,” in Procedia Engineering, 2014, vol. 89, pp. 21–28.
  • [16] N. Fontana, M. Giugni, L. Glielmo, G. Marini, and R. Zollo, “Real-Time Control of Pressure for Leakage Reduction in Water Distribution Network: Field Experiments,” J. Water Resour. Plan. Manag., vol. 144, no. 3, p. 04017096, Mar. 2018.
  • [17] Y. Kleiner, B. J. Adams, and J. S. Rogers, “Water distribution network renewal planning,” vol. 15, no. 1, pp. 15–26, Jan. 2001.
  • [18] E. Mann and J. Frey, “Optimized pipe renewal programs ensure cost-effective asset management,” in Pipelines 2011: A Sound Conduit for Sharing Solutions - Proceedings of the Pipelines 2011 Conference, 2011, pp. 44–54.
  • [19] I. Moslehi and M. Jalili_Ghazizadeh, “Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis,” Water Resour. Manag., vol. 34, no. 9, pp. 2851–2868, Jul. 2020.
  • [20] A. E. Akkaya and M. F. Talu, “Extended kalman filter based IMU sensor fusion application for leakage position detection in water pipelines,” J. Fac. Eng. Archit. Gazi Univ., vol. 32, no. 4, 2017.
  • [21] C. Uyanik, E. Erdemir, E. Kaplanoglu, and A. Sekmen, “A deep learning approach for motion segment estimation for pipe leak detection robot,” in Procedia Computer Science, 2019, vol. 158, pp. 37–44.
  • [22] S. Kazeminasab, V. Janfaza, M. Razavi, and M. K. Banks, “Smart Navigation for an In-pipe Robot Through Multi-phase Motion Control and Particle Filtering Method,” CoRR, vol. abs/2102.1, Feb. 2021.
  • [23] S. Kazeminasab, R. Jafari, and M. Katherine Banks, “An LQR-assisted Control Algorithm for an Under-actuated In-pipe Robot in Water Distribution Systems,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, 2021.
  • [24] W. Elmenreich, “Sensor Fusion in TimeTriggered Systems,” Austria, 2002.
  • [25] C. Li, C. Yang, J. Wan, A. S. Annamalai, and A. Cangelosi, “Teleoperation control of Baxter robot using Kalman filter-based sensor fusion,” Syst. Sci. Control Eng., vol. 5, no. 1, pp. 156–167, Jan. 2017.
  • [26] T. Zhang and Y. Liao, “Attitude measure system based on extended Kalman filter for multi-rotors,” Comput. Electron. Agric., vol. 134, pp. 19–26, Mar. 2017.
  • [27] Y. Liu, S. Gong, and Y. Lu, “Estimation of inertial/magnetic sensor orientation for human-motion-capture system,” 2017, pp. 175–179.
  • [28] S. Qiu, Z. Wang, H. Zhao, K. Qin, Z. Li, and H. Hu, “Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion,” Inf. Fusion, vol. 39, pp. 108–119, Jan. 2018.
  • [29] R. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Trans. ASME – J. Basic Eng., no. 82 (Series D), pp. 35–45, 1960.
  • [30] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 3rd editio. Wiley-IEEE, 2008.
  • [31] G. Bishop and G. Welch, “An Introduction to the Kalman Filter.” University of North Carolina SIGGRAPH 2001 course notes. ACM Inc., North Carolina, 2001.
  • [32] S. O. H. Madgwick, A. J. L. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” 2011, pp. 1–7.
  • [33] N. Trawny and S. I. Roumeliotis, “Indirect Kalman Filter for 3D Attitude Estimation A Tutorial for Quaternion Algebra Multiple Autonomous Robotic Systems Laboratory, Technical report,” Mar. 2005.
  • [34] W. H. K. de Vries, H. E. J. Veeger, C. T. M. Baten, and F. C. T. van der Helm, “Magnetic distortion in motion labs, implications for validating inertial magnetic sensors,” Gait Posture, vol. 29, no. 4, pp. 535–541, Jun. 2009.
  • [35] C. Wang, X. Qu, X. Zhang, W. Zhu, and G. Fang, “A Fast Calibration Method for Magnetometer Array and the Application of Ferromagnetic Target Localization,” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1743–1750, Jul. 2017.
  • [36] A. M. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Trans. Biomed. Eng., vol. 53, no. 7, pp. 1346–1356, Jul. 2006.
  • [37] Ruoyu Zhi, “A Drift Eliminated Attitude & Position Estimation Algorithm In 3D,” The University of Vermont, 2016.

Yıl 2021, Cilt: 25 Sayı: 5, 1102 - 1120, 30.10.2021

Abdullah Erhan Akkaya Muhammed Fatih Talu Ömür Aydoğmuş

https://doi.org/10.16984/saufenbilder.898072

Öz

Proje Numarası

215E075

Kaynakça

  • [1] R. McKenzie and C. Seago, “Assessment of real losses in potable water distribution systems: Some recent developments,” in Water Science and Technology: Water Supply, 2005, vol. 5, no. 1, pp. 33–40.
  • [2] P. Marin, B. Kingdom, and R. Liemberger, “The challenge of reducing non-revenue water (NRW) in developing countries - how the private sector can help : a look at performance-based service contracting,” Dec. 2006.
  • [3] S. M. Hooda, “Rajasthan Water Assessment : Potential for Private Sector Interventions,” New Delhi, © World Bank, 2017.
  • [4] “Infrastructure Report Card 2017.” [Online]. Available: https://www.infrastructurereportcard.org/ wp-content/uploads/2017/01/DrinkingWater-Final.pdf. [Accessed: 20-May2021].
  • [5] N. Pizzi and W. Lauer, Water distribution operator training handbook - Fourth Edition, 3rd ed. American Water Works Association, 2013.
  • [6] D. Misiunas, “Failure monitoring and asset condition assessment in water supply systems,” Sweden, 2005.
  • [7] O. Hunaidi, A. Wang, M. Bracken, T. Gambino, and C. Fricke, “Acoustic methods for locating leaks in municipal water pipe networks,” 2004, pp. 1–14.
  • [8] A.W.W.A., Water Audits and Loss Control Programs(M36): AWWA Manual of Practice, 4 edition. Denver: American Waterworks Association, 2016.
  • [9] S. H. Zyoud, L. G. Kaufmann, H. Shaheen, S. Samhan, and D. Fuchs-Hanusch, “A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS,” Expert Syst. Appl., vol. 61, pp. 86–105, Nov. 2016.
  • [10] S. Eggimann et al., “The Potential of Knowing More: A Review of Data-Driven Urban Water Management,” Environmental Science and Technology, vol. 51, no. 5. American Chemical Society, pp. 2538–2553, 07-Mar-2017.
  • [11] A. Gupta and K. D. Kulat, “A Selective Literature Review on Leak Management Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion Techniques for Water Distribution System,” Water Resources Management, vol. 32, no. 10. Springer Netherlands, pp. 3247–3269, 01-Aug-2018.
  • [12] M. FIRAT, S. YILMAZ, and C. ORHAN, “Su kayıp yönetimi için temel hesaplama araçlarının geliştirilmesi ve temel su kayıp bileşenlerinin analizi,” Gümüşhane Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 11, no. 2, pp. 405–416, Feb. 2021.
  • [13] M. Eugine, “Predictive Leakage Estimation using the Cumulative Minimum Night Flow Approach,” Am. J. Water Resour., vol. 5, no. 1, pp. 1–4, Jan. 2017.
  • [14] E. Farah and I. Shahrour, “Leakage Detection Using Smart Water System: Combination of Water Balance and Automated Minimum Night Flow,” Water Resour. Manag., vol. 31, no. 15, pp. 4821– 4833, Dec. 2017.
  • [15] E. Roshani and Y. Filion, “WDS leakage management through pressure control and pipes rehabilitation using an optimization approach,” in Procedia Engineering, 2014, vol. 89, pp. 21–28.
  • [16] N. Fontana, M. Giugni, L. Glielmo, G. Marini, and R. Zollo, “Real-Time Control of Pressure for Leakage Reduction in Water Distribution Network: Field Experiments,” J. Water Resour. Plan. Manag., vol. 144, no. 3, p. 04017096, Mar. 2018.
  • [17] Y. Kleiner, B. J. Adams, and J. S. Rogers, “Water distribution network renewal planning,” vol. 15, no. 1, pp. 15–26, Jan. 2001.
  • [18] E. Mann and J. Frey, “Optimized pipe renewal programs ensure cost-effective asset management,” in Pipelines 2011: A Sound Conduit for Sharing Solutions - Proceedings of the Pipelines 2011 Conference, 2011, pp. 44–54.
  • [19] I. Moslehi and M. Jalili_Ghazizadeh, “Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis,” Water Resour. Manag., vol. 34, no. 9, pp. 2851–2868, Jul. 2020.
  • [20] A. E. Akkaya and M. F. Talu, “Extended kalman filter based IMU sensor fusion application for leakage position detection in water pipelines,” J. Fac. Eng. Archit. Gazi Univ., vol. 32, no. 4, 2017.
  • [21] C. Uyanik, E. Erdemir, E. Kaplanoglu, and A. Sekmen, “A deep learning approach for motion segment estimation for pipe leak detection robot,” in Procedia Computer Science, 2019, vol. 158, pp. 37–44.
  • [22] S. Kazeminasab, V. Janfaza, M. Razavi, and M. K. Banks, “Smart Navigation for an In-pipe Robot Through Multi-phase Motion Control and Particle Filtering Method,” CoRR, vol. abs/2102.1, Feb. 2021.
  • [23] S. Kazeminasab, R. Jafari, and M. Katherine Banks, “An LQR-assisted Control Algorithm for an Under-actuated In-pipe Robot in Water Distribution Systems,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, 2021.
  • [24] W. Elmenreich, “Sensor Fusion in TimeTriggered Systems,” Austria, 2002.
  • [25] C. Li, C. Yang, J. Wan, A. S. Annamalai, and A. Cangelosi, “Teleoperation control of Baxter robot using Kalman filter-based sensor fusion,” Syst. Sci. Control Eng., vol. 5, no. 1, pp. 156–167, Jan. 2017.
  • [26] T. Zhang and Y. Liao, “Attitude measure system based on extended Kalman filter for multi-rotors,” Comput. Electron. Agric., vol. 134, pp. 19–26, Mar. 2017.
  • [27] Y. Liu, S. Gong, and Y. Lu, “Estimation of inertial/magnetic sensor orientation for human-motion-capture system,” 2017, pp. 175–179.
  • [28] S. Qiu, Z. Wang, H. Zhao, K. Qin, Z. Li, and H. Hu, “Inertial/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion,” Inf. Fusion, vol. 39, pp. 108–119, Jan. 2018.
  • [29] R. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Trans. ASME – J. Basic Eng., no. 82 (Series D), pp. 35–45, 1960.
  • [30] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 3rd editio. Wiley-IEEE, 2008.
  • [31] G. Bishop and G. Welch, “An Introduction to the Kalman Filter.” University of North Carolina SIGGRAPH 2001 course notes. ACM Inc., North Carolina, 2001.
  • [32] S. O. H. Madgwick, A. J. L. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” 2011, pp. 1–7.
  • [33] N. Trawny and S. I. Roumeliotis, “Indirect Kalman Filter for 3D Attitude Estimation A Tutorial for Quaternion Algebra Multiple Autonomous Robotic Systems Laboratory, Technical report,” Mar. 2005.
  • [34] W. H. K. de Vries, H. E. J. Veeger, C. T. M. Baten, and F. C. T. van der Helm, “Magnetic distortion in motion labs, implications for validating inertial magnetic sensors,” Gait Posture, vol. 29, no. 4, pp. 535–541, Jun. 2009.
  • [35] C. Wang, X. Qu, X. Zhang, W. Zhu, and G. Fang, “A Fast Calibration Method for Magnetometer Array and the Application of Ferromagnetic Target Localization,” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1743–1750, Jul. 2017.
  • [36] A. M. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Trans. Biomed. Eng., vol. 53, no. 7, pp. 1346–1356, Jul. 2006.
  • [37] Ruoyu Zhi, “A Drift Eliminated Attitude & Position Estimation Algorithm In 3D,” The University of Vermont, 2016.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yazılım Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Abdullah Erhan Akkaya 0000-0001-6193-5166

Muhammed Fatih Talu 0000-0003-1166-8404

Ömür Aydoğmuş 0000-0001-8142-1146

Proje Numarası 215E075
Yayımlanma Tarihi 30 Ekim 2021
Gönderilme Tarihi 16 Mart 2021
Kabul Tarihi 9 Haziran 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 25 Sayı: 5

Kaynak Göster

APA Akkaya, A. E., Talu, M. F., & Aydoğmuş, Ö. (2021). Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion. Sakarya University Journal of Science, 25(5), 1102-1120. https://doi.org/10.16984/saufenbilder.898072
AMA Akkaya AE, Talu MF, Aydoğmuş Ö. Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion. SAUJS. Ekim 2021;25(5):1102-1120. doi:10.16984/saufenbilder.898072
Chicago Akkaya, Abdullah Erhan, Muhammed Fatih Talu, ve Ömür Aydoğmuş. “Position Estimation of In-Pipe Robot Using Artificial Neural Network and Sensor Fusion”. Sakarya University Journal of Science 25, sy. 5 (Ekim 2021): 1102-20. https://doi.org/10.16984/saufenbilder.898072.
EndNote Akkaya AE, Talu MF, Aydoğmuş Ö (01 Ekim 2021) Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion. Sakarya University Journal of Science 25 5 1102–1120.
IEEE A. E. Akkaya, M. F. Talu, ve Ö. Aydoğmuş, “Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion”, SAUJS, c. 25, sy. 5, ss. 1102–1120, 2021, doi: 10.16984/saufenbilder.898072.
ISNAD Akkaya, Abdullah Erhan vd. “Position Estimation of In-Pipe Robot Using Artificial Neural Network and Sensor Fusion”. Sakarya University Journal of Science 25/5 (Ekim 2021), 1102-1120. https://doi.org/10.16984/saufenbilder.898072.
JAMA Akkaya AE, Talu MF, Aydoğmuş Ö. Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion. SAUJS. 2021;25:1102–1120.
MLA Akkaya, Abdullah Erhan vd. “Position Estimation of In-Pipe Robot Using Artificial Neural Network and Sensor Fusion”. Sakarya University Journal of Science, c. 25, sy. 5, 2021, ss. 1102-20, doi:10.16984/saufenbilder.898072.
Vancouver Akkaya AE, Talu MF, Aydoğmuş Ö. Position Estimation of In-Pipe Robot using Artificial Neural Network and Sensor Fusion. SAUJS. 2021;25(5):1102-20.
 Kapak Resmi İndir

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