Correction of Navigational Information Supplied to Biomimetic Autonomous Underwater Vehicle

Open access

Abstract

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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 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/non-Gaussian 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 : Model-Aided 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

Search
Journal information
Impact Factor

IMPACT FACTOR 2018: 1.214
5-year IMPACT FACTOR: 1.086

CiteScore 2018: 1.48

SCImago Journal Rank (SJR) 2018: 0.391
Source Normalized Impact per Paper (SNIP) 2018: 1.141

Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 436 106 3
PDF Downloads 132 74 3