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A Receding Horizon Control Approach to Navigation in Virtual Corridors

  • Domokos Kiss
  • Gábor Tevesz
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 1)

Abstract

Applications in mobile robotics require safe and goal-oriented motion while navigating in an environment obstructed by obstacles. The dynamic window approach (DWA) to collision avoidance and its different variants provide safe motion among obstacles, although they have the same limitation, namely using an objective function consisting of weighted terms. Different situations require different weights; however, there is no algorithm for choosing them. The Global Dynamic Window Approach with Receding Horizon Control (GDWA/RHC) presented in this chapter is similar to DWA but it uses a global navigation function (NF) and a receding horizon control scheme for guiding the robot. In order to make the calculation of the navigation function computationally tractable it is constructed by interpolation from a discrete function. In addition to that the domain of the navigation function is restricted to a virtual corridor between the start and goal positions of the robot.

Keywords

Mobile Robot Model Predictive Control Obstacle Avoidance Goal Point Visibility Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Latombe, J.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)CrossRefGoogle Scholar
  2. 2.
    Laumond, J.P.: Robot Motion Planning and Control. LNCIS, vol. 229. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)Google Scholar
  4. 4.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4, 23–33 (1997)CrossRefGoogle Scholar
  5. 5.
    Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proceedings of IEEE International Conference on Robotics and Automation, Sacramento, CA, USA, pp. 1398–1404 (1991)Google Scholar
  6. 6.
    Borenstein, J., Koren, Y.: The vector field histogram – fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7, 278–288 (1991)CrossRefGoogle Scholar
  7. 7.
    Brock, O., Khatib, O.: High-speed navigation using the global dynamic window ap-proach. In: Proceedings of IEEE International Conference on Robotics and Automation, Detroit, MI, USA, pp. 341–346 (1999)Google Scholar
  8. 8.
    Ogren, P., Leonard, N.E.: A convergent dynamic window approach to obstacle avoidance. IEEE Trans. Robot. 21, 188–195 (2005)CrossRefGoogle Scholar
  9. 9.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
  10. 10.
    Kiss, D., Tevesz, G.: A receding horizon control approach to obstacle avoidance. In: Proceedings of 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, pp. 397–402 (2011), doi:10.1109/SACI.2011.5873035Google Scholar
  11. 11.
    Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press (2010)Google Scholar
  12. 12.
    Liu, Y.H., Arimoto, S.: Path planning using a tangent graph for mobile robots among polygonal and curved obstacles. Int. J. Robot. Res. 11, 376–382 (1992)CrossRefGoogle Scholar
  13. 13.
    Kiss, D., Tevesz, G.: Efficient calculation of navigation functions for obstacle avoidance. In: Proceedings of Automation and Applied Computer Science Workshop, Budapest, Hungary, pp.128–139 (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Automation and Applied InformaticsBudapest University of Technology and EconomicsBudapestHungary

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