An Efficient Random Walk Strategy for Sampling Based Robot Motion Planners

  • Titas Bera
  • M. Seetharama Bhat
  • Debasish Ghose
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 103)


Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.


Randomized Algorithm Robot Motion Planning PRM Random Walk 


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  1. 1.
    Lozano Perez, T.: Spatial planning: A configuration space approach. IEEE Transaction on Computers 32(2), 108–120 (1983)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Reif, J.H.: Complexity of the mover’s problem and generalization. In: Proc. of FOCS, pp. 421–427 (1979)Google Scholar
  3. 3.
    Carpin, S.: Randomized motion planning - a tutorial. International Journal of Robotics and Automation 21(3), 184–196 (2006)CrossRefGoogle Scholar
  4. 4.
    Choset, H., et al.: Principles of Robot Motion: Theory algorithms and implementation. MIT Press, Cambridge (2005)MATHGoogle Scholar
  5. 5.
    Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmap for path planning in high dimensional configuration spaces. IEEE Transaction on Robotics and Automation 12(4), 566–580 (1996)CrossRefGoogle Scholar
  6. 6.
    Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings of the 1999 IEEE International Conference on Robotics and Automation, pp. 1018–1023 (1999)Google Scholar
  7. 7.
    Sun, Z., Hsu, D., Jiang, T., Kurniawati, H., Reif, J.H.: Narraow Passage Sampling for Probabilistic Roadmap Planning. IEEE Transaction on Robotics 21(5), 1105–1115 (2005)Google Scholar
  8. 8.
    Bera, T., Bhat, M.S., Ghose, D.: Preprocessing configuration space for improved sampling based path planning. In: ICEAE, Bangalore, India (2009)Google Scholar
  9. 9.
    Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D.: OBPRM: An obstacle-based PRM for 3D workspaces. In: Proc. 3rd Workshop Algorithmic Found. Robot., pp. 155–168 (1998)Google Scholar
  10. 10.
    Wilmarth, S.A., Amato, N.M., Stiller, P.F.: Motion planning for a rigid body using random networks on the medial axis of the free space. In: Proc. 15th Annu. ACM Symp. Computational Geometry, pp. 173–180 (1999)Google Scholar
  11. 11.
    Karatazas, I., Shreve, S.E.: Brownian Motion and Stochastic Calculus. Springer, Heidelberg (1988)Google Scholar
  12. 12.
    Hughes, B.D.: Random Walk and Random Environments, vol. 1. Clearson Press, Oxford (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Titas Bera
    • 1
  • M. Seetharama Bhat
    • 1
  • Debasish Ghose
    • 1
  1. 1.Department of Aerospace EngineeringIndian Institute of ScienceIndia

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