Voronoi-Based Heuristic for Nonholonomic Search-Based Path Planning

  • Qi Wang
  • Markus Wulfmeier
  • Bernardo Wagner
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


This paper proposes the use of a Voronoi-based heuristic to significantly speed up search-based nonholonomic path planning. Using generalized Voronoi diagrams (GVD) and in this manner exploiting geometric information about the obstacles, the presented approach is able to considerably reduce computation time while satisfying differential constraints using motion primitives for exploration. A key advantage compared to the common use of Euclidean heuristics is the inherent ability to avoid local minima of the cost function, which can be caused by, e.g., concave obstacles. Therefore, the application of the Voronoi-based heuristic is particularly beneficial in densely cluttered environments.


Search-based planning Nonholonomic planning Voronoi GVD Heuristic Primitive motion A* 


  1. 1.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1 (1959) 269271.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. In: IEEE Transactions on Systems Science and Cybernetics. Volume 4. (1968) 100107.Google Scholar
  3. 3.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2002).Google Scholar
  4. 4.
    Likhachev, M., Gordon, G., Thrun, S.: ARA*: Anytime A* with provable bounds on sub-optimality. In: Advances in Neural Information Processing Systems. (2003).Google Scholar
  5. 5.
    Zhou, R., Hansen, E.: Multiple sequence alignment using A*. In: Proceedings of the National Conference on Artificial Intelligence. (2002).Google Scholar
  6. 6.
    Chakrabarti, P., Ghosh, S., DeSarkar, S.: Admissiblility of AO* when heuristics overestimate. Artificial Intelligence 34 (1988) 97–113.CrossRefzbMATHGoogle Scholar
  7. 7.
    Korf, R.: Linear-space best-first search. Artificial Intelligence 62 (1993) 41–78.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Takahashi, O., Schilling, R.J.: Motion planning in a plane using generalized voronoi diagrams. IEEE T. Robotics and Automation 5(2) (1989) 143–150.CrossRefGoogle Scholar
  9. 9.
    Mirtich, B., Canny, J.: Using skeletons for nonholonomic path planning among obstacles (1992).Google Scholar
  10. 10.
    Garca, D.A.L., Gomez-Bravo, F.: Vodec: A fast voronoi algorithm for car-like robot path planning in dynamic scenarios. Robotica 30(7) (2012) 1189–1201.CrossRefGoogle Scholar
  11. 11.
    Shih, F.Y., Wu, Y.T.: Fast euclidean distance transformation in two scans using a 3x3 neighborhood. Computer Vision and Image Understanding 93(2) (2004) 195–205.Google Scholar
  12. 12.
    Wang, Q., Langerwisch, M., Wagner, B.: Wide range global path planning for a large number of networked mobile robots based on generalized voronoi diagrams. In: the 3rd IFAC Symposium on Telematics Applications. (October 2013).Google Scholar
  13. 13.
    Knowles, D., Murray, M.R.: Real time continuous curvature path planner for an autonomous vehicle in an urban environment (2006).Google Scholar
  14. 14.
    A. Kelly, M.P.: Generating near minimal spanning control sets for constrained motion planning in discrete state spaces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. (August 2005) 3231–3237.Google Scholar
  15. 15.
    Smith, D.E., Starkey, J.M.: Effects of model complexity on the performance of automated vehicle steering controllers: Model development, validation and comparison. Vehicle System Dynamics 24(2) (1995) 163–181.CrossRefGoogle Scholar
  16. 16.
    Lau, B., Sprunk, C., Burgard, W.: Improved updating of euclidean distance maps and voronoi diagrams. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings. (2010) 281–286.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Leibniz University of HannoverHannoverGermany

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