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Correction of navigational information supplied to biomimetic Autonomous Underwater Vehicle

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
PL
Abstrakty
EN
In order to autonomously transfer from one point of the environment to the other, Autonomous Underwater Vehicles (AUV) need a navigational system. While navigating underwater the vehicles usually use a dead reckoning method which calculates vehicle movement on the basis of the information about velocity (sometimes also acceleration) and course (heading) provided by on-board devicesl ike Doppler Velocity Logs and Fibre Optical Gyroscopes. Due to inaccuracies of the devices and the influence of environmental forces, the position generated by the dead reckoning navigational system (DRNS) is not free from errors, moreover the errors grow exponentially in time. The problem becomes even more serious when we deal with small AUVs which do not have any speedometer on board and whose course measurement device is inaccurate. To improve indications of the DRNS the vehicle can emerge onto the surface from time to time, record its GPS position, and measure position error which can be further used to estimate environmental influence and inaccuracies caused by mechanisms of the vehicle. This paper reports simulation tests which were performed to determine the most effective method for correction of DRNS designed for a real Biomimetic AUV.
Słowa kluczowe
Rocznik
Tom
Strony
13--23
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
  • Polish Naval Academy Institute of Naval Weapon Śmidowicza 69, 81-127 Gdynia Poland
Bibliografia
  • 1. B. Allotta, A. Caiti, L. Chisci, R. Costanzi, F. Di Corato, C. Fantacci, D. Fenucci, E. Meli, A. Ridolfi : Development of a Navigation Algorithm for Autonomous Underwater Vehicles. IFAC-PapersOnLine, Vol. 48, Is. 2, 2015, pp. 64-69
  • 2. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp: A tutorial on particle filters for on-line non-linear/nonGaussian Bayesian tracking. IEEE Trans, Signal Processing, 50 (2), 2002, pp. 174–188
  • 3. D. Li, D. Ji, J. Liu, Y. Lin : A Multi-Model EKF Integrated Navigation Algorithm for Deep Water AUV. International Journal of Advanced Robotic Systems, Vol. 13, Is. 1, 2016
  • 4. A. Doucet, N. de Freitas, N. Gordon: Sequential Monte Carlo methods in practice. Statistics for Engineering and Information Science. Springer-Verlag, New York, 2001
  • 5. G. Einicke, L. White: Robust Extended Kalman Filtering. IEEE Trans. Signal Processing. 47 (9), 1999, pp. 2596–2599
  • 6. H. Johnnsson, M. Kaess, B. Englot, F. Hover, JJ. Leonard: Imaging Sonar-Aided Navigation for Autonomous Underwater Harbor Surveillance. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 4396 – 4403
  • 7. S. Julier, J. Uhlmann : Unscented filtering and nonlinear estimation. Proc. of the IEEE, 92, 2004, pp. 401–422.
  • 8. J. Kinsey, R. Eustice, L. Whitcomb: A survey of underwater vehicle navigation: recent advances and new challenges. In Proc. Conf. Maneuvering Control Marine Craft. 2006, pp. 1–12
  • 9. T. Leszczynski : The effect of interference parameters on the exploitation capabilities of an underwater vehicle . Scientific Journal of Polish Naval Academy, 3(26), 2016, pp. 85-106
  • 10. L. Paull, S. Saeedi, M. Seto, H. Li : AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering, Volume: 39, Issue: 1, 2014, pp. 131 – 149
  • 11. M. Malec, M. Morawski, and J. Zając: Fish-like swimming prototype of mobile underwater robot. Journal of Automation, Mobile Robotics and Intelligent Systems, Vol. 4, No. 3, 2010, pp. 25-30
  • 12. A. Martinez, L. Hernandez, H. Sahli, Y. Valeriano-Medina, M. Orozco-Monteagudo, D. Garcia-Garcia : ModelAided Navigation with Sea Current Estimation for an Autonomous Underwater Vehicle. International Journal of Advanced Robotic Systems, Vol.12, Is. 7, DOI: https:// doi.org/10.5772/60415
  • 13. T. Praczyk : A quick algorithm for planning a path for biomimetic autonomous underwater vehicle. Scientific Journals of Maritime University of Szczecin, No. 45 (117), 2016, pp. 23-28
  • 14. T. Praczyk : Using Genetic Algorithms for Optimizing Algorithmic Control System of Biomimetic Underwater Vehicle. Computational Methods in Science and Technology (CMST), Vol. 21 (4) 2015, pp. 251-260
  • 15. R. Siegwart, I. R. Nourbakhsh : Introduction to Autonomous Mobile Robots. MIT Press, Cambridge 2004
  • 16. P. Szymak, T. Praczyk, K. Naus, M. Malec, M. Morawski : Research on biomimetic underwater vehicles for underwater ISR. Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, edited by Michael A. Kolodny, Tien Pham, Proc. of SPIE Vol. 9831, 98310K
  • 17. P. Szymak, M. Malec, M. Morawski : Directions of development of underwater vehicle with undulating propulsion. Polish Journal of Environmental Studies, Hard Publishing Company, Vol.19, No. 3, Olsztyn 2010, pp. 107-110
  • 18. E. Wan, R. Merwe : The Unscented Kalman Filter. T. Haykin Edition, New York, NY, USA, 2001
  • 19. W. Zeng, L. Wan, T. Zhang : Simultaneous localization and mapping of autonomous underwater vehicle using looking forward sonar. Journal of Shanghai Jiaotong University (Science), Vol. 17, 2012 , Is. 1, pp 91–97.
  • 20. T. Zhang, W. Zeng, and L. Wan : Underwater simultaneous localization and mapping based on forward--looking sonar. Journal of Marine Science and Application, (2011) pp.10:371. doi:10.1007/s11804-011-1082-1
  • 21. http://cmtm.pg.gda.pl/systemy-techniki-glebinowej
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-811fc59f-efa3-4d6f-973e-36b75a25ff16
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