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Finetuning Randomized Heuristic Search for 2D Path Planning: Finding the Best Input Parameters for R* Algorithm through Series of Experiments

  • Konstantin Yakovlev
  • Egor Baskin
  • Ivan Hramoin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8722)

Abstract

Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm’s best performance.

Keywords

path planning grid 2D A* R* heuristic search 

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References

  1. 1.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  2. 2.
    Yap, P.: Grid-based path-finding. In: Cohen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNBI), vol. 2338, pp. 44–55. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Tozour, P.: Search space representations. In: Rabin, S. (ed.) AI Game Programming Wisdom, vol. 2, pp. 85–102. Charles River Media (2004)Google Scholar
  4. 4.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  5. 5.
    Pearl, J.: Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley (1984)Google Scholar
  6. 6.
    Likhachev, M., Stentz, A.: R* Search. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence. AAAI Press, Menlo Park (2008)Google Scholar
  7. 7.
    Pohl, I.: First results on the effect of error in heuristic search. In: Bernard, M., Michie, D. (eds.) Machine Intelligence, vol. 5, pp. 219–236. Edinburgh University Press, Edinburg (1970)Google Scholar
  8. 8.
    Gallab, M., Dennis, A.: Aε – an efficient near admissible heuristic search algorithm. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI 1983), pp. 789–791 (1983)Google Scholar
  9. 9.
    Likhachev, M., Gordon, G., Thrun, S.: ARA*: Anytime A* with Provable Bounds on Sub-Optimality, Advances in Neural Information Processing Systems 16 (NIPS). MIT Press, Cambridge (2004)Google Scholar
  10. 10.
    Korf, R.E.: Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence 27(1), 97–109 (1985)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Reinefeld, A., Marsland, T.A.: Enhanced iterative-deepening search. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(7), 701–710 (1994)CrossRefGoogle Scholar
  12. 12.
    Bisiani, R.: Beam search. In: Shapiro, S. (ed.) Encyclopedia of Artificial Intelligence, pp. 56–58. John Wiley and Sons (1987)Google Scholar
  13. 13.
    Zhang, W.: Complete anytime beam search. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), pp. 425–430 (1998)Google Scholar
  14. 14.
    Botea, A., Muller, M., Schaeffer, J.: Near optimal hierarchical path finding. Journal of Game Development 1(1), 7–28 (2004)Google Scholar
  15. 15.
    Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation 12(4), 566–580 (1996)CrossRefGoogle Scholar
  16. 16.
    LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning, Technical Report, 98-11, Computer Science Dept., Iowa State University (1998)Google Scholar
  17. 17.
    Likhachev, M., Stentz, A.: R* search: The proofs. Technical Report, University of Pennsylvania, Philadelphia, PA (2008b)Google Scholar
  18. 18.
    Pitteway, M.L.V.: Algorithms of conic generation. In: Fundamental Algorithms for Computer Graphics, pp. 219–237. Springer, Heidelberg (1985)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Konstantin Yakovlev
    • 1
  • Egor Baskin
    • 1
  • Ivan Hramoin
    • 1
  1. 1.Institute for Systems Analysis of Russian Academy of SciencesMoscowRussia

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