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Collaborative Aquatic Positioning System Utilising Multi-beam Sonar and Depth Sensors (2403.10397v2)

Published 15 Mar 2024 in cs.RO

Abstract: Accurate positioning of remotely operated underwater vehicles (ROVs) in confined environments is crucial for inspection and mapping tasks and is also a prerequisite for autonomous operations. Presently, there are no positioning systems available that are suited for real-world use in confined underwater environments, unconstrained by environmental lighting and water turbidity levels and have sufficient accuracy for long-term, reliable and repeatable navigation. This shortage presents a significant barrier to enhancing the capabilities of ROVs in such scenarios. This paper introduces an innovative positioning system for ROVs operating in confined, cluttered underwater settings, achieved through the collaboration of an omnidirectional surface vehicle and an ROV. A formulation is proposed and evaluated in the simulation against ground truth. The experimental results from the simulation form a proof of principle of the proposed system and also demonstrate its deployability. Unlike many previous approaches, the system does not rely on fixed infrastructure or tracking of features in the environment and can cover large enclosed areas without additional equipment.

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References (31)
  1. C. Zhao, P. R. Thies, and L. Johanning, “Offshore inspection mission modelling for an ASV/ROV system,” Ocean Engineering, vol. 259, p. 111899, 2022.
  2. A. Griffiths, A. Dikarev, P. R. Green, B. Lennox, X. Poteau, and S. Watson, “AVEXIS—aqua vehicle explorer for in-situ sensing,” IEEE Robotics and Automation Letters, vol. 1, no. 1, pp. 282–287, 2016.
  3. D. A. Duecker, A. R. Geist, E. Kreuzer, and E. Solowjow, “Learning environmental field exploration with computationally constrained underwater robots: Gaussian processes meet stochastic optimal control,” Sensors, vol. 19, no. 9, p. 2094, 2019.
  4. P. Ozog, N. Carlevaris-Bianco, A. Kim, and R. M. Eustice, “Long-term mapping techniques for ship hull inspection and surveillance using an autonomous underwater vehicle,” Journal of Field Robotics, vol. 33, no. 3, pp. 265–289, 2016.
  5. X. M. Lv, Y. F. Liu, H. B. Gao, L. Ding, J. G. Tao, K. R. Xia, and Z. Q. Deng, “Design of underwater welding robot used in nuclear plant,” in Key Engineering Materials, vol. 620, pp. 484–489, Trans Tech Publ, 2014.
  6. S. Watson, D. A. Duecker, and K. Groves, “Localisation of unmanned underwater vehicles (UUVs) in complex and confined environments: A review,” Sensors, vol. 20, no. 21, p. 6203, 2020.
  7. U. K. Verfuss, A. S. Aniceto, D. V. Harris, D. Gillespie, S. Fielding, G. Jiménez, P. Johnston, R. R. Sinclair, A. Sivertsen, S. A. Solbø, et al., “A review of unmanned vehicles for the detection and monitoring of marine fauna,” Marine pollution bulletin, vol. 140, pp. 17–29, 2019.
  8. T. Sakaue, T. Nagakita, T. Kaneda, Y. Yamashita, K. Nishizawa, K. Kanbara, H. Hanaoka, S. Shirai, S. Kikuchi, and D. Uchijima, “Development of USV Used in Underground Floors Surveying of the Contaminated Buildings at Fukushima Daiichi NPS,” in IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 224–229, 2022.
  9. R. R. Khan, T. Taher, and F. S. Hover, “Accurate geo-referencing method for auvs for oceanographic sampling,” in OCEANS 2010 MTS/IEEE SEATTLE, pp. 1–5, 2010.
  10. M. Morgado, P. Oliveira, and C. Silvestre, “Tightly coupled ultrashort baseline and inertial navigation system for underwater vehicles: An experimental validation,” Journal of Field Robotics, vol. 30, no. 1, pp. 142–170, 2013.
  11. Sonardyne, “Micro-ranger 2 USBL.” https://www.sonardyne.com/products/micro-ranger-2-shallow-water-usbl-system/. accessed: 2023-03-15.
  12. S. Rahman, A. Quattrini Li, and I. Rekleitis, “Svin2: A multi-sensor fusion-based underwater slam system,” The International Journal of Robotics Research, vol. 41, no. 11-12, pp. 1022–1042, 2022.
  13. S. Leutenegger, S. Lynen, M. Bosse, R. Siegwart, and P. Furgale, “Keyframe-based visual–inertial odometry using nonlinear optimization,” The International Journal of Robotics Research, vol. 34, no. 3, pp. 314–334, 2015.
  14. T. Qin, P. Li, and S. Shen, “VINS-Mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics, vol. 34, no. 4, pp. 1004–1020, 2018.
  15. C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM,” IEEE Transactions on Robotics, vol. 37, no. 6, pp. 1874–1890, 2021.
  16. D. Ribas, P. Ridao, J. Neira, and J. D. Tardos, “SLAM using an imaging sonar for partially structured underwater environments,” in IEEE/RSJ international conference on intelligent robots and systems, pp. 5040–5045, IEEE, 2006.
  17. A. Mallios, P. Ridao, D. Ribas, M. Carreras, and R. Camilli, “Toward autonomous exploration in confined underwater environments,” Journal of Field Robotics, vol. 33, no. 7, pp. 994–1012, 2016.
  18. D. A. Duecker, N. Bauschmann, T. Hansen, E. Kreuzer, and R. Seifried, “Towards micro robot hydrobatics: Vision-based guidance, navigation, and control for agile underwater vehicles in confined environments,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1819–1826, 2020.
  19. Qualisys, “Miqus.” https://www.qualisys.com/cameras/miqus/, 2022. accessed: 2023-03-15.
  20. J. Snyder, “Doppler velocity log navigation for observation-class ROVs,” Sea Technology, vol. 51, no. 12, pp. 27–30, 2010.
  21. G. Fukuda, D. Hatta, X. Guo, and N. Kubo, “Performance evaluation of imu and dvl integration in marine navigation,” Sensors, vol. 21, no. 4, p. 1056, 2021.
  22. L. Luo, Y. Huang, Z. Zhang, and Y. Zhang, “A New Kalman Filter-Based In-Motion Initial Alignment Method for DVL-Aided Low-Cost SINS,” IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 331–343, 2021.
  23. D. Wang, X. Xu, Y. Yao, T. Zhang, and Y. Zhu, “A novel SINS/DVL tightly integrated navigation method for complex environment,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 5183–5196, 2020.
  24. R. H. Rogne, T. H. Bryne, T. I. Fossen, and T. A. Johansen, “MEMS-based inertial navigation on dynamically positioned ships: Dead reckoning,” IFAC-PapersOnLine, vol. 49, no. 23, pp. 139–146, 2016.
  25. M. Sung, J. Kim, H. Cho, M. Lee, and S.-C. Yu, “Underwater-sonar-image-based 3d point cloud reconstruction for high data utilization and object classification using a neural network,” Electronics, vol. 9, no. 11, p. 1763, 2020.
  26. M. D. Aykin and S. Negahdaripour, “Forward-look 2d sonar image formation and 3d reconstruction,” in 2013 OCEANS-San Diego, pp. 1–10, IEEE, 2013.
  27. K. Groves, A. West, K. Gornicki, S. Watson, J. Carrasco, and B. Lennox, “MallARD: An autonomous aquatic surface vehicle for inspection and monitoring of wet nuclear storage facilities,” Robotics, vol. 8, no. 2, p. 47, 2019.
  28. Unity Technologies, “Unity.” https://unity.com/. accessed: 2024-02-05.
  29. I. Lončar, J. Obradović, N. Kraševac, L. Mandić, I. Kvasić, F. Ferreira, V. Slošić, Đ. Nađ, and N. Mišković, “MARUS-a marine robotics simulator,” in OCEANS 2022, Hampton Roads, pp. 1–7, IEEE, 2022.
  30. NWH Coding, “Dynamic Water Physics 2.” https://assetstore.unity.com/packages/tools/physics/dynamic-water-physics-2-147990. accessed: 2024-02-05.
  31. Open Robotics, “ROS.” http://wiki.ros.org/. accessed: 2023-03-15.

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