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Path planning is typically considered in Artificial Intelligence as a graphsearching problem and R* is state-of-the-art algorithm tailored to solve it.The algorithm decomposes given path finding task into the series of subtaskseach of which can be easily (in computational sense) solved by well-knownmethods (such as A*). Parameterized random choice is used to perform thedecomposition and as a result R* performance largely depends on the choice ofits input parameters. In our work we formulate a range of assumptionsconcerning possible upper and lower bounds of R* parameters, theirinterdependency and their influence on R* performance. Then we evaluate theseassumptions by running a large number of experiments. As a result we formulatea 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.

Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments