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

TL;DR

This paper investigates how to optimize the input parameters of the R* path planning algorithm using extensive experiments, resulting in heuristic rules that improve its performance in 2D pathfinding tasks.

Contribution

It introduces a systematic approach to fine-tune R* parameters through experimental analysis, enhancing its efficiency in 2D path planning.

Findings

Heuristic rules for parameter initialization improve R* performance.

Parameter bounds and dependencies significantly affect algorithm efficiency.

Experimental evaluation validates the proposed parameter tuning approach.

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.