On-Line Obstacle Detection Using Data Range for Reactive Obstacle Avoidance

  • José Miguel Vilca
  • Lounis Adouane
  • Youcef Mezouar
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

This paper deals with the reactive navigation in clustered environment. It proposes an online and adaptive elliptic trajectory to perform smooth and safe mobile robot navigation. These trajectories use limit-cycle principle already applied in the literature [3]. The main contribution proposed here is to perform this navigation in a completely reactive way while using only range sensor data. At this aim, each obstacle to avoid is surrounded by an ellipse and its parameters are obtained online while using the sequential range data and appropriate method to identify the enclosed ellipse. Different methods to obtain these ellipse parameters are presented and implemented. A specific criterion is taken into account to obtain always smooth change in these parameters. A large number of simulations permit to show the efficiency of our proposal for the navigation in cluttered environment.

Keywords

Mobile robot navigation Obstacle detection and avoidance Telemetry Parameter identification Least square 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adouane, L.: Hybrid and safe control architecture for mobile robot navigation. In: 9th Conference on Autonomous Robot Systems and Competitions, Portugal (2009)Google Scholar
  2. 2.
    Adouane, L.: Orbital obstacle avoidance algorithm for reliable and on-line mobile robot navigation. In: 9th Conference on Autonomous Robot Systems and Competitions, Portugal (2009)Google Scholar
  3. 3.
    Adouane, L., Benzerrouk, A., Martinet, P.: Mobile robot navigation in cluttered environment using reactive elliptic trajectories. In: 18th IFAC World Congress (2011)Google Scholar
  4. 4.
    Adouane, L., Le Fort-Piat, N.: Behavioral and distributed control architecture of control for minimalist mobile robots. Journal Europen des Systémes Automatisés 40(2), 177–196 (2006)CrossRefGoogle Scholar
  5. 5.
    Benzerrouk, A., Adouane, L., Martinet, P.: Lyapunov global stability for a reactive mobile robot navigation in presence of obstacles. In: ICRA 2010 International Workshop on Robotics and Intelligent Transportation System (2010)Google Scholar
  6. 6.
    Brooks, R.A.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation RA-2, 14–23 (1986)Google Scholar
  7. 7.
    De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.: The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems 50(1), 1–18 (2000)CrossRefGoogle Scholar
  8. 8.
    Eberly, D.: Distance from a point to an ellipse in 2d. In: Geometric Tools, LLC (2008), http://www.geometrictools.com/
  9. 9.
    Egerstedt, M., Hu, X.: A hybrid control approach to action coordination for mobile robots. Automatica 38(1), 125–130 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Jie, M.S., Baek, J.H., Hong, Y.S., Lee, K.W.: Real time obstacle avoidance for mobile robot using limit-cycle and vector field method. Knowledge-Based Intelligent Information and Engineering Systems, 866–873 (2006)Google Scholar
  11. 11.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5, 90–99 (1986)CrossRefGoogle Scholar
  12. 12.
    Kim, D.H., Kim, J.H.: A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer. Robotics and Autonomous Systems 42(1), 17–30 (2003)MATHCrossRefGoogle Scholar
  13. 13.
    Toibero, J., Carelli, R., Kuchen, B.: Switching control of mobile robots for autonomous navigation in unknown environments. In: IEEE International Conference on Robotics and Automation, pp. 1974–1979 (2007)Google Scholar
  14. 14.
    Welzl, E.: Smallest Enclosing Disks (Balls and Ellipsoids). In: Maurer, H.A. (ed.) New Results and New Trends in Computer Science. LNCS, vol. 555, pp. 359–370. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  15. 15.
    Zhang, Z.: Parameter estimation techniques: A tutorial with application to conic fitting. Image and Vision Computing 15, 59–76 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Miguel Vilca
    • 1
  • Lounis Adouane
    • 1
  • Youcef Mezouar
    • 1
  1. 1.Pascal UBP – UMR CNRS 6602Clermont-FerrandFrance

Personalised recommendations