Motion Planning for Car-Like Robots Using Lazy Probabilistic Roadmap Method

  • Abraham Sánchez L.
  • René Zapata
  • J. Abraham Arenas B.
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2313)

Abstract

In this paper we describe an approach to probabilistic roadmap method. Our algorithm builds initially a roadmap in the configuration space considering that all nodes and edges are collision-free, and searches the roadmap for the shortest path between start and goal nodes. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap or the planner updates the roadmap with new nodes and edges, and then searches for a shortest path. The procedure is repeated until a collision-free path is found. The goal of our approach is to minimize the number of collision checks and calls to the local method. Experimental results presented in this paper show that our approach is very efficient in practice.

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References

  1. 1.
    Overmars, M. H.: A random approach to motion planning. Technical Report RUU-CS-92-32, Utrecht University (1992)Google Scholar
  2. 2.
    Overmars, M. H., Švestka, P.: A probabilistic learning approach to motion planning. Workshop on the Algorithmic Foundations of Robotics. A. K. Peters (1994) 19–37Google Scholar
  3. 3.
    Kavraki, L. E., Švestka, P., Latombe, J-C., Overmars, M. H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation. Vol 12, No. 4 (1996) 566–579CrossRefGoogle Scholar
  4. 4.
    Švestka, P., Overmars, M. H.: Motion planning for car-like robots using a probabilistic learning approach. The International Journal of Robotics Research. Vol 16, No. 2 (1997) 119–143CrossRefGoogle Scholar
  5. 5.
    Nissoux, C., Siméon, T., Laumond, J. P.: Visibility based probabilistic roadmaps. IEEE International Conference on Intelligent Robots and Systems (1999)Google Scholar
  6. 6.
    Kuffner, J. J., LaValle, S. M.: RRT-Connect: An efficient approach to singlequery path planning. IEEE International Conference on Robotics and Automation (2000)Google Scholar
  7. 7.
    Vallejo, D., Jones, C., Amato, N. M.: An adaptive framework for’ single-shot’ motion planning. Technical Report 99-024. Texas A&M University (1999)Google Scholar
  8. 8.
    Bohlin, R., Kavraki, L. E.: Path planning using lazy PRM. IEEE International Conference on Robotics and Automation (2000) 521–528Google Scholar
  9. 9.
    Sánchez, A. G., Latombe, J-C.: A single-query bi-directional probabilistic roadmap planner with lazy collision checking. Int. Symposium on Robotics Research (ISRR’01) (2001)Google Scholar
  10. 10.
    Laumond, J-P Ed.: Robot motion planning and control. Springer Verlag (1988)Google Scholar
  11. 11.
    Reeds, J. A., Shepp, R. A.: Optimal paths for a car that goes both forward and backwards. Pacific Journal of Mathematics. 145(2) (1990) 367–393MathSciNetGoogle Scholar
  12. 12.
    Sánchez, L. A., Arenas, B. J. A., Zapata, R.: Optimizing trajectories in nonholonomic motion planning. In 3er Encuentro Internacional de Ciencias de la Computación. INEGI (2001) 479–488Google Scholar
  13. 13.
    Vendittelli, M., Laumond, J. P., Nissoux, C.: Obstacle distance for car-like robots. IEEE Transactions on Robotics and Automation. Vol 15, No. 4 (1999) 678–691CrossRefGoogle Scholar
  14. 14.
    Souères, P., Boissonnat, J-D.: Optimal trajectories for non-holonomic robots. In Robot motion planning and control, J. P. Laumond Ed. Vol 229, Springer Verlag (1998)Google Scholar
  15. 15.
    Desaulniers, G.: On shortest paths for a car-like robot maneuvering around obstacles. Robotics and Autonomous Systems. Vol 17 (1996) 139–148CrossRefGoogle Scholar
  16. 16.
    Reif, J., Wang, H.: The complexity of the two dimensional curvature-constrained shortest-path problem. Robotics: The algorithmic perspective. P. K. Agarwal et al. Eds, A. K. Peters (1998)Google Scholar
  17. 17.
    Agarwal, P. K., Raghavan, P., Tamaki, H.: Motion planning for a steering-constrained robot through moderate obstacles. In ACM Symp. on Computational Geometry (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Abraham Sánchez L.
    • 1
  • René Zapata
    • 1
  • J. Abraham Arenas B.
    • 2
  1. 1.LIRMM, UMR5506 CNRSMontpellier Cedex 5France
  2. 2.Facultad de Ciencias de la ComputaciónBUAPPue.México

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