Skip to main content
Log in

A real-time navigation system for autonomous underwater vehicle

  • Review Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

This paper focuses on the study and implementation of a navigation system in order to estimate position, velocity and attitude of an autonomous underwater vehicle, AUV. The extended Kalman filter, EKF, is investigated for the fusion of the sample data from different sensors: the strapdown inertial measurement unit, magnetic compass, Doppler velocity log, depth sensor, and an acoustic positioning system. Results are applied to the development of a navigation system for the Pirajuba AUV, an autonomous underwater vehicle that is being developed at the mechatronics department of the Politechnic School of the University of Sao Paulo. The navigation system is composed by off the shelf components integrated in a CAN based network. On the hardware platform, a software architecture is implemented based on free and largely known tools, like C language, and the GNU compiler. The real-time performance of the filter is validated through laboratory and field tests. The last one includes experiment using an automobile vehicle. Results in the field tests indicate the correct choice for the system model assumed in the EKF, and the good performance of the navigation algorithm in real-time. During the simulation, the accuracy obtained in the estimation of the AUV position and attitude are satisfactory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Brown RG, Hwang PYC (1997) Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions, 3rd edn. Wiley, USA

    Google Scholar 

  2. Dantas JD, de Barros EA (2009) A real-time simulator for AUV development. In: Proceedings of the 20th Brazilian Congress of Mechanical Engineering, Gramado, RS, Brazil

  3. De Barros EA, Freire L, Dantas JLD (2010) Development of Pirajuba AUV. In: Proceedings of CAMS2010

  4. Douglass BP (2007) Real-time UML workshop for embedded systems. News, Oxford, p 408

    Google Scholar 

  5. Jakuba MV et al. (2008) Long-baseline acoustic navigation for under-ice AUV operations. J Field Robot, Wiley Periodicals 25:861–879

  6. Jouffroy J, Opderbecke J (2007) Underwater vehicle navigation using diffusion-based trajectory observers. IEEE J Ocean Eng 32(2):313–326

    Article  Google Scholar 

  7. Kuga HK (1981) Estimação Adaptativa de Órbitas Aplicada a Satélites a Baixa Altitude. Master Thesis in Space Science, Instituto Nacional de Pesquisas Espaciais, INPE

  8. Labrosse JJ (2002) MicroC/OS-II the real-time kernel, 2nd edn. CMP Books, USA

    Google Scholar 

  9. Lapointe CEG (2006) Virtual long baseline (VLBL) autonomous underwater vehicle navigation using a single transponder. Thesis, Massachusetts Institute of Technology, Massachusetts, USA

  10. Larsen MB (2006) The autonomous redundant navigation system of an AUV for mine counter measures. In: Proceedings of Undersea Defense Technology, UDT2006

  11. Larsen MB (2001) Autonomous navigation of underwater vehicles. Dissertação (Ph.D), Department of Automation Technical University of Denmark, USA

  12. Lee PM, Jun BH (2007) Pseudo long base line navigation algorithm for underwater vehicles with inertial sensors and two acoustic range measurements. Ocean Eng 34:416–425

    Article  Google Scholar 

  13. Mcewen R et al. (2003) Performance of an AUV navigation system at arctic latitudes. In: Proceedings MTS/IEEE Int. Conf. OCEANS, vol 31, pp 642–653

  14. MISRA (1998) Guidelines for the use of the C language in Vehicle Based Software, 1st edn. The motor Industry Research Association, England

    Google Scholar 

  15. Nygren I, Jansson M (2004) Terrain navigation for underwater vehicle using the correlator method. IEEE J Ocean Eng 29(3):906–915

    Article  Google Scholar 

  16. Santana DDS (2011) Navegação Terrestre Usando Unidade de Medição Inercial de Baixo Desempenho e Fusão Sensorial com Filtro de Kalman Adaptativo Suavizado. PhD Thesis, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil

  17. Silvestre CJF (2000) Multi-objective optimization theory with applications to the integrated design of controllers/plants for autonomous vehicles. Dissertation, Department of Electrical and Computer Engineering, Technical Superior Institute, Lisbon, Portugal

  18. Trigo FC (2005) Estimação Não Linear de Parâmetros através dos Filtros de Kalman na Tomografia por Impedância Elétrica. Doctoral Thesis, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil

  19. Zhao L, Gao W (2004) The experimental study on GPS/INS/DVL integration for AUV. Position Location and Navigation Symposium, PLANS 2004, pp 337–340

Download references

Acknowledgments

Authors would like to acknowledge the Fapesp for the financial support of this research (Proc. No 2009/10205-3). Fabio D. Zanoni is sponsored by CAPES master students scholarship. The authors thank all the members of Laboratory for Unmanned Vehicles at University of São Paulo, João Lucas Dozzi Dantas, Lucas de Oliveira, and Rodrigo Vale, for their support in carrying out the tests with the navigation system.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fábio Doro Zanoni.

Additional information

Technical Editor: Glauco A. de P. Caurin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zanoni, F.D., de Barros, E.A. A real-time navigation system for autonomous underwater vehicle. J Braz. Soc. Mech. Sci. Eng. 37, 1111–1127 (2015). https://doi.org/10.1007/s40430-014-0231-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-014-0231-2

Keywords

Navigation