DEVELOPMENT OF AUTONOMOUS UNDERWATER VEHICLE (AUV) BASED ON ROBOTIC OPERATING SYSTEM FOR FOLLOWING UNDERWATER CABLE

Authors

  • Mardiyanto Ronny Institut Teknologi Sepuluh Nopember, Indonesia

DOI:

https://doi.org/10.21107/kursor.v11i3.274

Keywords:

Marine Cable Communication Channels, AUV, Thresholding, Contour Detection

Abstract

In addition to satellites, the Marine Cable Communication Channel (SKKL) which is located under the sea is also one of the backbones of the communication network to connect from one island to another. However, there are often irresponsible parties who damage or commit acts of vandalism. This action resulted in the disruption of the communication process on the submarine cable network. So, it is necessary to periodically check the condition of the underwater communication network cables. Regular checking of underwater cables is very risky, so we propose an underwater robot to handle it. This paper presents the development of Autonomous Underwater Vehicle (AUV) based on Robotic Operating System (ROS) for Following Underwater Cable to monitor the condition of the cables automatically. The AUV is equipped with a camera, Nvidia Jetson Nano, Arduino, Flight Controller, ESC, and brushless DC motors that used to assist the tracking process on the cable. The camera is used as the main visual sensor. Visual image processing methods are carried out using thresholding and contours detection methods, then the obtained data are processed to drive the motors on the AUV so that they can move on the direction of the cable. The experiment results show that the object detection method can be used under conditions with light intensity more than 25 lux. It works optimally at speeds of 0.27 m/s to 0.42 m/s. In the horizontal motion control test, the overshoot parameter value is ±60%, rise time is 2s, settling time is 16s, and steady state error is ±20%. The AUV can track on a straight and winding path of 2 meters with a bright light intensity of ±493 lux, a dim light of ±107 lux and a dark light intensity of ±25 lux with the help of an LED beam with a light intensity of ±773 lux. The percentage of success of scoping experiments on a straight track and a winding track with three trials is 75%. This performance shows that the developed AUV works well to follow underwater cable.

Downloads

Download data is not yet available.

References

[1] Y. Ito, N. Kato, J. Kojima, S. Takagi, K. Asakawa, and Y. Shirasaki, “Cable tracking for autonomous underwater vehicle,” in Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV’94), Jul. 1994, pp. 218–224. doi: 10.1109/AUV.1994.518628.
[2] D. L. Msongaleli, F. Dikbiyik, M. Zukerman, and B. Mukherjee, “Disaster-Aware Submarine Fiber-Optic Cable Deployment for Mesh Networks,” J. Light. Technol., vol. 34, no. 18, pp. 4293–4303, Sep. 2016, doi: 10.1109/JLT.2016.2587719.
[3] S. Ren, “Evaluation of Ship Traffic Control Safety Based on Analytic Hierarchy Process,” in 2010 International Conference on Intelligent Computation Technology and Automation, May 2010, vol. 2, pp. 237–240. doi: 10.1109/ICICTA.2010.216.
[4] W. S. Mary, S. Muthukumar, V. Suganya Pragathi, E. R. Reshma, and R. Viiavaraghavan, “International Maritime Boundary Alert for Fishermen,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Mar. 2018, pp. 246–248. doi: 10.1109/ICECA.2018.8474726.
[5] T. J. B. H. K. K. dan I. RI, “Law of The Republic of Indonesia Number 36 of 1999 on Telecommunications.” https://jdih.kominfo.go.id/produk_hukum/view/id/564/t/undangundang+nomor+36+tahun+1999+tanggal+8+september+1999 (accessed Sep. 08, 2021).
[6] L. D. Jatmiko, “Kabel Laut Putus, Biaya Perbaikan Bisa Lebih dari Rp50 Miliar,” bisnis.com, Jakarta, p. Telekomunikasi, Aug. 15, 2019.
[7] H. M. Manik et al., “Autonomous Underwater Vehicle untuk Survei dan Pemantauan Laut,” J. Rekayasa Elektr., vol. 13, no. 1, Art. no. 1, Apr. 2017, doi: 10.17529/jre.v13i1.5964.
[8] K. Asakawa, J. Kojima, Y. Kato, S. Matsumoto, and N. Kato, “Autonomous underwater vehicle AQUA EXPLORER 2 for inspection of underwater cables,” in Proceedings of the 2000 International Symposium on Underwater Technology (Cat. No.00EX418), May 2000, pp. 242–247. doi: 10.1109/UT.2000.852551.
[9] J. O. Hallset, “Simple vision tracking of pipelines for an autonomous underwater vehicle,” in 1991 IEEE International Conference on Robotics and Automation Proceedings, Apr. 1991, pp. 2767–2772 vol.3. doi: 10.1109/ROBOT.1991.132050.
[10] V. I. Koshelev and D. N. Kozlov, “Wire recognition in image within aerial inspection application,” in 2015 4th Mediterranean Conference on Embedded Computing (MECO), Jun. 2015, pp. 159–162. doi: 10.1109/MECO.2015.7181891.
[11] J. Zhang, L. Liu, B. Wang, X. Chen, Q. Wang, and T. Zheng, “High Speed Automatic Power Line Detection and Tracking for a UAV-Based Inspection,” in 2012 International Conference on Industrial Control and Electronics Engineering, Aug. 2012, pp. 266–269. doi: 10.1109/ICICEE.2012.77.
[12] F. Tian, Y. Wang, and L. Zhu, “Power line recognition and tracking method for UAVs inspection,” in 2015 IEEE International Conference on Information and Automation, Aug. 2015, pp. 2136–2141. doi: 10.1109/ICInfA.2015.7279641.
[13] G. Zhou, J. Yuan, I. Yen, and F. Bastani, “Robust real-time UAV based power line detection and tracking,” in 2016 IEEE International Conference on Image Processing (ICIP), Sep. 2016, pp. 744–748. doi: 10.1109/ICIP.2016.7532456.
[14] S. Yan and L. Jin, “Extra Matters Recognition of Transmission System Based on Hough Transform,” in 2011 Asia-Pacific Power and Energy Engineering Conference, Mar. 2011, pp. 1–3. doi: 10.1109/APPEEC.2011.5748630.
[15] C. Pan, X. Cao, and D. Wu, “Power line detection via background noise removal,” in 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec. 2016, pp. 871–875. doi: 10.1109/GlobalSIP.2016.7905967.
[16] D. Bore, A. Rana, N. Kolhare, and U. Shinde, “Automated Guided Vehicle Using Robot Operating Systems,” in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Apr. 2019, pp. 819–822. doi: 10.1109/ICOEI.2019.8862716.
[17] “Development of a Long Range Autonomous Underwater Vehicle for Ocean Observation.” https://ieeexplore.ieee.org/document/9389473/ (accessed Sep. 08, 2021).
[18] “OpenCV: Graph API: Converting image from one color space to another.” https://docs.opencv.org/master/dc/d38/group__gapi__colorconvert.html#ga3e8fd8197ab16811caf9b31cb1e08a05 (accessed Sep. 08, 2021).
[19] “OpenCV: Image Thresholding.” https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html (accessed Sep. 08, 2021).
[20] “OpenCV: Contour Features.” https://docs.opencv.org/3.4/dd/d49/tutorial_py_contour_features.html (accessed Sep. 08, 2021).
[21] E. Chebotareva and L. Gavrilova, “Educational Mobile Robotics Project ‘ROS-Controlled Balancing Robot’ Based on Arduino and Raspberry Pi,” in 2019 12th International Conference on Developments in eSystems Engineering (DeSE), Oct. 2019, pp. 209–214. doi: 10.1109/DeSE.2019.00047.

Downloads

Published

2022-07-31

Issue

Section

Articles

Citation Check