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)


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.


path planning grid 2D A* R* heuristic search 


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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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