A Sampling Schema for Rapidly Exploring Random Trees using a Guiding Path

In this paper, a novel sampling schema for Rapidly Exploring Random Trees (RRT) is proposed to address the narrow passage issue. The introduced method employs a guiding path to steer the tree growth towards a given goal. The main idea of the proposed approach stands in a preference of the sampling of the configuration space C along a given guiding path instead of sampling of the whole space. While for a lowdimensional C the guiding path can be found as a geometric path in the robot workspace, such a path does not provide useful information for efficient sampling of a high-dimensional C. We propose an iterative scaling approach to find a guiding path in such high-dimensional configuration spaces. The approach starts with a scaled geometric model of the robot to a fraction of its original size for which a guiding path is found using the RRT algorithm. Then, such a path is iteratively used in the proposed RRT-Path algorithm for a larger robot up to its original size. The experimental results indicate benefit of the proposed technique in significantly higher ratio of the successfully found feasible paths in comparison to the state-of-the-art RRT algorithms.