ANALYSIS OF CONTEMPORARY METHODS AND PROSPECTS FOR THE DEVELOPMENT OF UNDERWATER VEHICLE NAVIGATION
DOI:
https://doi.org/10.26906/SUNZ.2023.4.005Keywords:
autonomous underwater vehicles, acoustic navigation, Long Baseline, Ultra Short Baseline, Short Baseline, Kalman filter, Doppler Velocity Log, algorithm for integrated navigation NARX-RKF, dead reckoning navigation method and inertial navigation systemsAbstract
This article conducts a comprehensive analysis of modern navigation methods for autonomous underwater vehicles, emphasizing their technological features, advantages, and limitations. Primary attention is given to five key methods: acoustic navigation, global positioning systems, Doppler velocity log (DVL) navigation, inertial navigation, trajectory observation navigation based on diffusion, as well as ensuring the navigation of groups of underwater vehicles. The study's results indicate that integrating various navigation methods can significantly enhance the reliability and accuracy of positioning underwater vehicles, ensuring effective mission execution in challenging conditions. Considering current technology development trends and operational requirements, the article also outlines directions for further research and developments in underwater navigation.Downloads
References
Kinsey J. C. A survey of underwater vehicle navigation: Recent advances and new challenges / James C. Kinsey, Ryan M. EUSTICE, Louis L. Whitcomb // IFAC conference of manoeuvering and control of marine craft. – 2006. – P. 1–12.
Smallwood D. A. A new hydrodynamics test facility for UUV dynamics and control research / D. A. Smallwood, J. C. Kinsey, L. L. Whitcomb // Proceedings of IEEE/MTS Oceans. – 2003. – P. 356–361.
Bingham B. Hypothesis Grids: Improving Long Baseline Navigation for Autonomous Underwater Vehicles [Electronic resource] / B. Bingham, W. Seering // IEEE Journal of Oceanic Engineering. – 2006. – Vol. 31, no. 1. – P. 209–218. – Mode of access: https://doi.org/10.1109/joe.2006.872220.
Inzartsev A. Underwater Vehicles / Alexander Inzartsev. – London: Intechopen, 2009. – 596 p.
USBL Integration and Assessment in a Multisensor Navigation Approach for AUVs 1 1This work is partially supported by Ministry of Economy and Competitiveness under contracts TIN2014-58662-R, DPI2014-57746-C3-2-R and FEDER funds. [Electronic resource] / Eric Guerrero Font [et al.] // IFAC-PapersOnLine. – 2017. – Vol. 50, no. 1. – P. 7905–7910. – Mode of access: https://doi.org/10.1016/j.ifacol.2017.08.754.
Dubrovin F. Navigation for AUV, located in the shadow area of LBL, during the group operations / Fedor Dubrovin // Global Oceans 2020: Singapore–US Gulf Coast. IEEE. – 2020. – P. 1–6.
Leonard J. J. Autonomous underwater vehicle navigation / John J. Leonard, Alexander Bahr // Springer handbook of ocean engineering. – 2016. – P. 341–358..
Wang Q. System noise variance matrix adaptive Kalman filter method for AUV INS/DVL navigation system [Electronic resource] / Qiuying Wang, Kaiyue Liu, Zhongyi Cao // Ocean Engineering. – 2023. – Vol. 267. – P. 113269. – Mode of access: https://doi.org/10.1016/j.oceaneng.2022.113269
An experimental comparison of Deep Learning strategies for AUV navigation in DVL-denied environments [Electronic resource] / Edoardo Topini [et al.] // Ocean Engineering. – 2023. – Vol. 274. – P. 114034. – Mode of access: https://doi.org/10.1016/j.oceaneng.2023.114034
An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network [Electronic resource] / Haifa Dai [et al.] // Defence Technology. – 2020. – Vol. 16, no. 2. – P. 334–340. – Mode of access: https://doi.org/10.1016/j.dt.2019.08.011
A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering [Electronic resource] / Xiaozhen Yan [et al.] // Sensors. – 2019. – Vol. 19, no. 20. – P. 4576. – Mode of access: https://doi.org/10.3390/s19204576
Navnet: AUV Navigation Through Deep Sequential Learning [Electronic resource] / Xin Zhang [et al.] // IEEE Access. – 2020. – Vol. 8. – P. 59845–59861. – Mode of access: https://doi.org/10.1109/access.2020.2982272
Petritoli E. High Accuracy Attitude and Navigation System for an Autonomous Underwater Vehicle (AUV) [Electronic resource] / Enrico Petritoli, Fabio Leccese // ACTA IMEKO. – 2018. – Vol. 7, no. 2. – P. 3. – Mode of access: https://doi.org/10.21014/acta_imeko.v7i2.535
Jouffroy J. Underwater Vehicle Navigation Using Diffusion-Based Trajectory Observers [Electronic resource] / JÉrÔme Jouffroy, Jan Opderbecke // IEEE Journal of Oceanic Engineering. – 2007. – Vol. 32, no. 2. – P. 313–326. – Mode of access: https://doi.org/10.1109/joe.2006.880392
Development of a Regional Underwater Positioning and Communication System for Control of Multiple Autonomous Underwater Vehicles / Sasano M., Inaba S., Okamoto A.—// Proc. of the IEEE/OES Autonomous Underwater Vehicles (AUV).— 2016.— № 6.— P. 431–434.
Experimental Validation of the Moving Long Base Line Navigation Concept / Vaganay J., Leonard J., Curcio J., Willcox S.— // Proc. of the Int. Conf. AUV.— 2004.— № 4.— P. 1–7.
An Algorithm for Cooperative Navigation of Multiple UUVs / Zhang L., Xu D., Liu M., Yan W., Gao J.—// Proc. of the sixth Int. Sym- posium on Underwater Technology, UT2009,— 2009.— № 78.— P. 1–6.
Comparing Kalman and particle filter approaches to coordinated multi-vehicle navigation / Mirabellot D., Sandersont A., Blidberg D.—// Proc. of the Int. Conf. UUST 2007.— 2007.— № 78.— P. 1–6.
