An Improved Rapidly Exploring Random Tree Algorithm for Path Planning in Configuration Spaces with Narrow Channels

TL;DR

This paper introduces an enhanced RRT algorithm that efficiently explores narrow channels in configuration spaces, resulting in shorter paths for robot motion planning compared to classical methods.

Contribution

The paper proposes a novel RRT variant that detects narrow channels and biases sampling within them to improve path quality in complex spaces.

Findings

Computes significantly shorter paths in narrow channel environments.

Outperforms classical RRT and variants in benchmark tests.

Improves exploration efficiency in challenging configuration spaces.

Abstract

Rapidly-exploring Random Tree (RRT) algorithms have been applied successfully to challenging robot motion planning and under-actuated nonlinear control problems. However a fundamental limitation of the RRT approach is the slow convergence in configuration spaces with narrow channels because of the small probability of generating test points inside narrow channels. This paper presents an improved RRT algorithm that takes advantage of narrow channels between the initial and goal states to find shorter paths by improving the exploration of narrow regions in the configuration space. The proposed algorithm detects the presence of narrow channel by checking for collision of neighborhood points with the infeasible set and attempts to add points within narrow channels with a predetermined bias. This approach is compared with the classical RRT and its variants on a variety of benchmark planning problems. Simulation results indicate that the algorithm presented in this paper computes a significantly shorter path in spaces with narrow channels.