Enhanced SIRRT*: A Structure-Aware RRT* for 2D Path Planning with Hybrid Smoothing and Bidirectional Rewiring

TL;DR

Enhanced SIRRT* introduces structure-aware techniques like hybrid smoothing and bidirectional rewiring to significantly improve path planning efficiency and reliability in complex environments.

Contribution

The paper proposes E-SIRRT*, a novel structure-aware extension of SIRRT*, combining hybrid smoothing and bidirectional rewiring for better convergence and path quality.

Findings

E-SIRRT* outperforms IRRT* and SIRRT* in initial path quality.

E-SIRRT* demonstrates faster convergence and higher robustness.

Deterministic initialization improves performance consistency.

Abstract

Sampling-based motion planners such as Rapidly-exploring Random Tree* (RRT*) and its informed variant IRRT* are widely used for optimal path planning in complex environments. However, these methods often suffer from slow convergence and high variance due to their reliance on random sampling, particularly when initial solution discovery is delayed. This paper presents Enhanced SIRRT* (E-SIRRT*), a structure-aware planner that improves upon the original SIRRT* framework by introducing two key enhancements: hybrid path smoothing and bidirectional rewiring. Hybrid path smoothing refines the initial path through spline fitting and collision-aware correction, while bidirectional rewiring locally optimizes tree connectivity around the smoothed path to improve cost propagation. Experimental results demonstrate that E-SIRRT* consistently outperforms IRRT* and SIRRT* in terms of initial path quality, convergence rate, and robustness across 100 trials. Unlike IRRT*, which exhibits high variability due to stochastic initialization, E-SIRRT* achieves repeatable and efficient performance through deterministic skeleton-based initialization and structural refinement.