Keywords: dynamic positioning, unmanned underwater vehicle, navigation system, visual odometry, control system
Concept of dynamic positioning system for unmanned small-class underwater vehicles based on visual odometry
UDC 629.584
DOI: 10.26102/2310-6018/2024.46.3.029
The article is devoted to the actual problem of underwater robotics - the problem of dynamic positioning of unmanned underwater vehicles of small class. Particular attention is paid to the methods of navigation of unmanned underwater vehicles and methods for creating a dynamic positioning system, including methods for the synthesis of an observer, a regulator and methods for distributing control actions on the propulsion and steering complex of unmanned underwater vehicles. It is revealed that in the existing dynamic positioning systems, expensive hydro acoustic navigation systems and Doppler speed meters are mainly used to generate feedback on the position and speed of unmanned underwater vehicles. Not all unmanned submersibles of the small class of the budget segment are equipped with such systems, while video systems and inertial sensors are present in almost every device. With the development of onboard computing facilities, it becomes possible to use visual odometry algorithms for navigation of unmanned underwater vehicles based on data from a video system as an alternative to hydro acoustic navigation in the task of dynamic positioning. The concept of architecture of the system of dynamic positioning of unmanned underwater vehicles of small class based on visual odometry is proposed, which helps to reduce the cost of navigation equipment and allows to increase the productivity of underwater technical work.
1. Cui W., Fu S., Hu Z. Encyclopedia of Ocean Engineering. Singapore: Springer; 2022. 2177 p. https://doi.org/10.1007/978-981-10-6946-8
2. Inzartsev A.V., Kiselev L.V., Kostenko V.V., Matvienko Yu.V., Pavin A.M., Shcherbatyuk A.F. Underwater robotics: systems, technologies, application. Vladivostok: Institute of Marine Technologies Problems Far Eastern Branch Russian Academy of Sciences; 2018. 368 p. (In Russ.).
3. Gladkova O.I., Veltischev V.V., Egorov S.A. The concept of an information control system of a remotely operated vehicle with combined propulsion system for vessels inspection without dry-docking. Izvestiya vysshikh uchebnykh zavedenii. Severo-Kavkazskii region. Tekhnicheskie nauki = Bulletin of Higher Educational Institutions. North Caucasus Region. Technical Sciences. 2020;(3):55–63. (In Russ.). https://doi.org/10.17213/1560-3644-2020-3-55-63
4. Efimov S.V., Knyazev S.I., Jatsun S.F. Investigation of Controlled Movement of the Small-Size Underwater Vehicle for Water Pollution Analyse. Cloud of science. 2020;7(3):488–497. (In Russ.).
5. Filaretov V.F., Konoplin A.Yu., Konoplin N.Yu. A System for an Automatic Implementation of the Manipulative Operations by Means of the Underwater Robots. Mekhatronika, avtomatizatsiya, upravlenie = Мechatronics, Automation, Control. 2017;18(8):543–549. (In Russ.). https://doi.org/10.17587/mau.18.543-549
6. Kostenko V.V., Pavin A.M. Automatic stabilization of unmanned underwater vehicles over the seabed objects on the base of photo images. Podvodnye issledovaniya i robototekhnika = Underwater Investigations and Robotics. 2014;(1):39–47. (In Russ.).
7. Dantsevich I.M., Tarasenko A.A. Semi-automatic control of remote-operated submersible devices while monitoring subsea pipelines. Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoi promyshlennosti = Automation, Telemechanization and Communication in Oil Industry. 2012;(12):42–47. (In Russ.).
8. Litvishko I.R. Dynamic Positioning of Autonomous Underwater Vehicle in Shallow Water under the Influence of External Disturbances. Sovremennye informatsionnye tekhnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2022;18(1):72–82. (In Russ.). https://doi.org/10.25559/SITITO.18.202201.72-82
9. Kostenko V.V., Lyakhov D.G., Mokeeva I.G. K voprosu otsenki effektivnosti ispol'zo-vaniya teleupravlyaemykh podvodnykh apparatov obsledovatel'skogo klassa. Tekhnicheskie problemy osvoeniya Mirovogo okeana. 2011;4:97–104. (In Russ.).
10. Filaretov V.F., Yukhimets D.A. Two-Loop System with Reference Model for Control of Spatial Movement of Cargo Underwater Vehicle. Mekhatronika, avtomatizatsiya, upravlenie = Мechatronics, Automation, Control. 2021;22(3):134–144. (In Russ.). https://doi.org/10.17587/mau.22.134-144
11. Dukan F., Ludvigsen M., Sørensen A.J. Dynamic positioning system for a small size ROV with experimental results. In: OCEANS 2011 IEEE – Spain, 06–09 June 2011, Santander, Spain. IEEE; 2011. pp. 1–10. https://doi.org/10.1109/Oceans-Spain.2011.6003399
12. Filaretov V.F., Yukhimets D.A., Zuev A.V., Zhirabok A.N. The development of AUV control system with accommodation to thruster faults. Robototekhnika i tekhnicheskaya kibernetika = Robotics and Technical Cybernetics. 2021;9(4):280–288. (In Russ.). https://doi.org/10.31776/RTCJ.9405
13. Hosseinnajad A., Loueipour M. Design of a Robust Observer-based DP Control System for an ROV with Unknown Dynamics Including Thruster Allocation. In: 2021 7th International Conference on Control, Instrumentation and Automation (ICCIA), 23–24 February 2021, Tabriz, Iran. IEEE; 2021. pp. 1–6. https://doi.org/10.1109/ICCIA52082.2021.9403543
14. Liu S., Wang D., Poh E.K. Dynamic positioning of AUVs in shallow water environment: observer and controller design. In: 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Proceedings, 24–28 July 2005, Monterey, USA. IEEE; 2005. pp. 705–710. https://doi.org/10.1109/AIM.2005.1511065
15. Liu S., Wang D., Poh E.K. Output feedback control design for station keeping of AUVs under shallow water wave disturbances. International Journal of Robust and Nonlinear Control. 2009;19(13):1447–1470. https://doi.org/10.1002/rnc.1387
16. Gao J., Liu Ch., Proctor A. Nonlinear model predictive dynamic positioning control of an underwater vehicle with an onboard USBL system. Journal of Marine Science and Technology. 2016;21(1):57–69. https://doi.org/10.1007/s00773-015-0332-3
17. Gao J., Wu P., Li T., Proctor A. Optimization-based model reference adaptive control for dynamic positioning of a fully actuated underwater vehicle. Nonlinear Dynamics. 2017;87(4):2611–2623. https://doi.org/10.1007/s11071-016-3214-2
18. Ohrem S.J., Amundsen H.B., Caharija W., Holden C. Robust adaptive backstepping DP control of ROVs. Control Engineering Practice. 2022;127. https://doi.org/10.1016/j.conengprac.2022.105282
19. Cao Y., Li B., Li Q., Stokes A.A., Ingram D.M., Kiprakis A. A Nonlinear Model Predictive Controller for Remotely Operated Underwater Vehicles With Disturbance Rejection. IEEE Access. 2020;8:158622–158634. https://doi.org/10.1109/ACCESS.2020.3020530
20. Bobkov V.A., Mashentsev V.Yu. Navigation of an Underwater Robot by Stereo Images. Mekhatronika, avtomatizatsiya, upravlenie = Мechatronics, Automation, Control. 2016;17(2):101–109. (In Russ.). https://doi.org/10.17587/mau.17.101-109
21. Ferrera M., Moras J., Trouvé-Peloux P., Creuze V. Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments. Sensors. 2019;19(3). https://doi.org/10.3390/s19030687
22. Zhang S., Zhao S., An D., Liu J., Wang H., Feng Y., Daoliang L., Zhao R. Visual SLAM for underwater vehicles: A survey. Computer Science Review. 2022;46. https://doi.org/10.1016/j.cosrev.2022.100510
23. Wang X., Fan X., Shi P., Ni J., Zhou Z. An Overview of Key SLAM Technologies for Underwater Scenes. Remote Sensing. 2023;15(10). https://doi.org/10.3390/rs15102496
24. Kostenko V.V., Tolstonogov A.Yu. Control allocation approaches for over-actuated underwater vehicles: a brief review. Podvodnye issledovaniya i robototekhnika = Underwater Investigations and Robotics. 2021;(1):4–17. (In Russ.). https://doi.org/10.37102/1992-4429_2021_35_01_01
25. Fossen T.I. Handbook of Marine Craft Hydrodynamics and Motion Control. Chichester: John Wiley & Sons, Ltd.; 2011. 600 p.
26. Boreiko A.A., Vaulin Yu.V., Kostenko V.V., Matvienko Yu.V., Mikhailov D.N., Pavin A.M. Navigation and algorithmic support AUV-ROV complex in solving problems monitoring bottom surface. Izvestiya YuFU. Tekhnicheskie nauki = Izvestiya SFedU. Engineering Sciences. 2014;(3):112–127. (In Russ.).
27. Vavilova N.B., Parusnikov N.A., Subkhankulova G.A. The navigation algorithm of the underwater vehicle with strapdown inertial navigation system. Trudy MAI. 2016;(89). (In Russ.). URL: https://www.elibrary.ru/download/elibrary_27174462_51258828.pdf
28. Yukhimets D.A., Gubankov A.S. Navigation system of an autonomous underwater vehicle based on data transmitted via an acoustic channel from a hydroacoustic station. Izvestiya YuFU. Tekhnicheskie nauki = Izvestiya SFedU. Engineering Sciences. 2023;(1):227–240. (In Russ.). https://doi.org/10.18522/2311-3103-2023-1-227-240
29. Hidalgo F., Kahlefendt C., Bräunl T. Monocular ORB-SLAM Application in Underwater Scenarios. In: 2018 OCEANS – MTS/IEEE Kobe Techno-Oceans (OTO), 28–31 May 2018, Kobe, Japan. IEEE; 2018. pp. 1–4. https://doi.org/10.1109/OCEANSKOBE.2018.8559435
30. Dabove P., Di Pietra V., Piras M. Monocular Visual Odometry with Unmanned Underwater Vehicle Using Low Cost Sensors. In: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 20–23 April 2020, Portland, USA. IEEE; 2020. pp. 810–816. https://doi.org/10.1109/PLANS46316.2020.9109841
31. Xu Z., Haroutunian M., Murphy A.J., Neasham J., Norman R. An Integrated Visual Odometry System for Underwater Vehicles. IEEE Journal of Oceanic Engineering. 2021;46(3):848–863. https://doi.org/10.1109/JOE.2020.3036710
32. Li M., Yang Ke., Qin J., Zhong J., Jiang Z., Su Q. Comparative study on real-time pose estimation of vision-based unmanned underwater vehicles. Cobot. 2023;2. https://doi.org/10.12688/cobot.17642.2
33. Campos C., Elvira R., Rodríguez J.J.G., Montiel J.M.M., Tardós J.D. ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM. IEEE Transactions on Robotics. 2021;37(6):1874–1890. https://doi.org/10.1109/TRO.2021.3075644
34. Zhan H., Weerasekera C.S., Bian J.-W., Garg R., Reid I. DF-VO: What Should Be Learnt for Visual Odometry? URL: https://arxiv.org/abs/2103.00933 [Accessed 30th August 2024].
35. Kinsey J.C., Yang Q., Howland J.C. Nonlinear Dynamic Model-Based State Estimators for Underwater Navigation of Remotely Operated Vehicles. IEEE Transactions on Control Systems Technology. 2014;22(5):1845–1854. https://doi.org/10.1109/TCST.2013.2293958
36. Hosseini M., Seyedtabaii S. Robust ROV path following considering disturbance and measurement error using data fusion. Applied Ocean Research. 2016;54:67–72. https://doi.org/10.1016/j.apor.2015.10.009
37. Jin X.-B., Robert Jeremiah R.J., Su T.-L., Bai Y.-T., Kong J.-L. The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods. Sensors. 2021;21(6). https://doi.org/10.3390/s21062085
38. Wang B., Chen C., Jiang Z., Zhao Y. ROV State Estimation Using Mixture of Gaussian Based on Expectation-Maximization Cubature Particle Filter. Applied Sciences. 2023;13(10). https://doi.org/10.3390/app13105885
Keywords: dynamic positioning, unmanned underwater vehicle, navigation system, visual odometry, control system
For citation: Aliagaev A. R., Azhmukhamedov I. M., Khomenko T. V., Concept of dynamic positioning system for unmanned small-class underwater vehicles based on visual odometry. Modeling, Optimization and Information Technology. 2024;12(3). URL: https://moitvivt.ru/ru/journal/pdf?id=1695 DOI: 10.26102/2310-6018/2024.46.3.029 (In Russ).
Received 16.09.2024
Revised 25.09.2024
Accepted 27.09.2024
Published 30.09.2024