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Parametric and Nonparametric Bayesian Filters for Autonomous Underwater Vehicle Localization

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Abstract

This work presents a comparison between parametric and nonparametric localization methods for autonomous underwater vehicles based on two classes of Bayesian filters for sensor fusion: the Particle Filter and the Extended Kalman Filter. In order to develop a localization method that does not require external sensors, the terrain-based localization technique is studied, which uses the particle filter and bathymetric information regarding the terrain. While promising, this approach has poor precision in regions with small depth variations. In order to improve this methodology, two solutions are presented: a software-based solution which uses a trajectory generation algorithm that limits the vehicle navigation to regions of the map with large depth variation, and a hardware-based solution which uses GPS intelligent buoy sensors. In order to analyze the convergence performance of the terrain-based localization with the trajectory generation algorithm, Monte Carlo simulations are performed with different quantities of particles. For comparison purposes, an Extended Kalman Filter fusing an inertial measurement unit and GPS intelligent buoys are also analyzed. Simulation results show that the triangulation-based approach achieves an improved performance, at the cost of extra sensors.

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References

  • Chaves, E. R, Jr., André, F. D. A., & Maitelli, A. L. (2019). Robust observer-based actuator and sensor fault estimation for discrete-time systems. Journal of Control, Automation and Electrical Systems, 30, 160–169.

    Article  Google Scholar 

  • da Silva, J. E., Terra, B., Martins, R., & de Sousa, J. B. (2007). Modeling and simulation of the LAUV autonomous underwater vehicle. In 13th IEEE IFAC international conference on methods and models in automation and robotics.

  • França, R. P., Salton, A. T., Castro, R. D., Green, B. N., & Marelli, D. (2015). Trajectory generation for bathymetry based AUV navigation and localization. IFAC-PapersOnLine, 48(16), 95–100.

    Article  Google Scholar 

  • González, J., Blanco, J. L., Galindo, C., Ortiz-de Galisteo, A., Fernandez-Madrigal, J. A., Moreno, F. A., et al. (2009). Mobile robot localization based on ultra-wide-band ranging: A particle filter approach. Robotics and Autonomous Systems, 57(5), 496–507.

    Article  Google Scholar 

  • Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE Proceedings F (radar and signal processing), IET, vol. 140, pp. 107–113.

  • Hastings, W. K. (1970). Monte carlo sampling methods using markov chains and their applications. Biometrika, 57(1), 97–109.

    Article  MathSciNet  Google Scholar 

  • Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Fluids Engineering, 82(1), 35–45.

    MathSciNet  Google Scholar 

  • Kalman, R. E., & Bucy, R. S. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering, 83(1), 95–108.

    Article  MathSciNet  Google Scholar 

  • Karlsson, R., Gusfafsson, F., & Karlsson, T. (2003). Particle filtering and Cramer-Rao lower bound for underwater navigation. In Acoustics, speech, and signal processing, 2003. Proceedings.(ICASSP’03). 2003 IEEE international conference on, IEEE, vol. 6, pp. VI-65.

  • Klein, I., & Diamant, R. (2018). Observability analysis of heading aided INS for a maneuvering AUV. Journal of the Institute of Navigation, 65(1), 73–82.

    Article  Google Scholar 

  • Lima, J. M., Guetter, A. K., Freitas, S. R., Panetta, J., & de Mattos, J. G. Z. (2017). A meteorologicalstatistic model for short-term wind power forecasting. Journal of Control, Automation and Electrical Systems, 28(5), 679–691.

    Article  Google Scholar 

  • Matiussi Ramalho, G., Carvalho, S. R., Finardi, E. C., & Moreno, U. F. (2018). Trajectory optimization using sequential convex programming with collision avoidance. Journal of Control, Automation and Electrical Systems, 29(3), 318–327.

    Article  Google Scholar 

  • Mohammaddadi, G., Pariz, N., & Karimpour, A. (2017). Modal Kalman filter. Asian Journal of Control, 19(2), 728–738.

    Article  MathSciNet  Google Scholar 

  • Mori, H., & Kikuchi, T. (2017). Performance verification of underwater crawling swimming robot with attitude changing function. Electronics and Communications in Japan, 100(10), 70–81.

    Article  Google Scholar 

  • Munafò, A., & Ferri, G. (2017). An acoustic network navigation system. Journal of Field Robotics, 34(7), 1332–1351.

    Article  Google Scholar 

  • Nemra, A., & Aouf, N. (2010). Robust INS/GPS sensor fusion for UAV localization using SDRE nonlinear filtering. IEEE Sensors Journal, 10(4), 789–798.

    Article  Google Scholar 

  • Neto, W. A., Pinto, M. F., Marcato, A. L. M., da Silva, I. C., & Fernandes, Dd A. (2019). Mobile robot localization based on the novel leader-based bat algorithm. Journal of Control, Automation and Electrical Systems, 30(3), 337–346.

    Article  Google Scholar 

  • Rauschenbach, T., Pfützenreuter, T., & Ament, C. (2015). Editorial of the special issue of underwater robotics. Robotics and Autonomous Systems, 67(C), 1–2.

    Article  Google Scholar 

  • Scardua, L. A., & da Cruz, J. J. (2016). Particle-based tuning of the unscented kalman filter. Journal of Control, Automation and Electrical Systems, 27, 10–18.

    Article  Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • USGS. (2012). Lake Tahoe bathymetry, united states geological survey. http://tahoe.usgs.gov/bath.html. Accessed 03 June 2017.

  • Wolbrecht, E., Gill, B., Borth, R., Canning, J., Anderson, M., & Edwards, D. (2014). Hybrid baseline localization for autonomous underwater vehicles. Journal of Intelligent & Robotic Systems, pp. 1–19.

  • Won, S. P., Melek, W. W., & Golnaraghi, F. (2010). A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system. IEEE Transactions on Industrial Electronics, 57(5), 1787–1798.

    Article  Google Scholar 

  • Wynn, R. B., Huvenne, V. A., Le Bas, T. P., Murton, B. J., Connelly, D. P., Bett, B. J., et al. (2014). Autonomous underwater vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Marine Geology, 352, 451–468.

    Article  Google Scholar 

  • Youngberg, J. W. (1992). Method for extending GPS to underwater applications. US Patent 5,119,341.

  • Zhou, Z., Wu, J., Li, Y., Fu, C., & Fourati, H. (2017). Critical issues on Kalman filter with colored and correlated system noises. Asian Journal of Control, 19(6), 1905–1919.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. GEPOC - Center of Technology, Federal University of Santa Maria, Santa Maria, Brazil

    Rodrigo P. França

  2. GACS - School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil

    Guilherme A. Pimentel

  3. DELAE, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Aurélio T. Salton

Authors
  1. Rodrigo P. França
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  2. Guilherme A. Pimentel
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  3. Aurélio T. Salton
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Correspondence to Rodrigo P. França.

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This study was partly financed by CAPES - Finance Code 001, FAPERGS 13/1896-1 and CNPq 306214/2018-0, Brazil.

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França, R.P., Pimentel, G.A. & Salton, A.T. Parametric and Nonparametric Bayesian Filters for Autonomous Underwater Vehicle Localization. J Control Autom Electr Syst 31, 40–51 (2020). https://doi.org/10.1007/s40313-019-00529-z

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  • DOI: https://doi.org/10.1007/s40313-019-00529-z

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