Skip to main content

Advertisement

Springer Nature Link
Log in

State estimation of mobile robot using GPS signal that includes reflected and diffracted waves

  • Special Feature: Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

We propose a method to improve the state estimation accuracy of mobile robots placed near high-rise buildings using the statistical property of the reflected and diffracted waves of global positioning system (GPS) signals. First, it is assumed that a GPS signal that contains a reflected and diffracted wave is denoted by the sum of the true position information and noise that follows a time-varying Gaussian distribution. On the basis of this assumption, the time-varying bias of a GPS signal is tracked using a Kalman filter. In addition, a particle filter, which executes sampling and likelihood evaluation using the estimated bias, is developed. With the proposed method, a GPS signal that contains the rejected noise introduced by the conventional method can be used efficiently, and the state estimation accuracy of the robot in a shadow area of GPS satellite can be improved. Furthermore, a control system for an autonomous mobile robot incorporating the proposed state estimation mechanism is developed, and its effectiveness is evaluated via simulation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Hofmann-Wellenhof B, Lichtenegger H, Collins J (2001) GPS: theory and practice, 5th revised edn. Springer, NewYork, pp 125–129

  2. Yamazaki M, Takeuchi E, Ohono K, Tadokoro S (2011) GPS based particle filter localization method with multipath model using 3D-Map. JRSJ 29(8):702–709

    Article  Google Scholar 

  3. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge, pp 117–234

  4. Maier D, Kleiner A (2010) Improved GPS sensor model for mobile robots in urban terrain. In: International conference on robotics and automation

  5. Bekey GA (2005) Autonomous robots: from biological inspiration to implementation and control. MIT Press, Cambridge, pp 57–71

  6. Kitagawa G (1998) Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J Comput Graph Stat 5:1–25

    MathSciNet  Google Scholar 

  7. Doucet A, Freitas N, Gordon N (eds) (2001) Sequential Monte Carlo methods in practice. Springer, Berlin

  8. Tomono M (2011) Probabilistic approaches to localization and mapping for mobile robots. JRSJ 29(5):423–426

    Article  Google Scholar 

  9. Corke P (2011) Robotics, vision and control fundamental algorithms in MATLAB. Springer, Berlin, pp 65–130

  10. Liu JS, Chen R (1998) Sequential Monte Carlo methods for dynamic systems. J Am Stat Assoc 93(443):1032–1044

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu, Fukuoka, Japan

    Takeshi Nishida & Shinichi Sagara

  2. Department of Basic Sciences, Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu, Fukuoka, Japan

    Sozo Inoue

Authors
  1. Takeshi Nishida
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sozo Inoue
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shinichi Sagara
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Takeshi Nishida.

About this article

Cite this article

Nishida, T., Inoue, S. & Sagara, S. State estimation of mobile robot using GPS signal that includes reflected and diffracted waves. Artif Life Robotics 18, 178–186 (2013). https://doi.org/10.1007/s10015-013-0115-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-013-0115-z

Keywords