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

2025-05-28

AI Summary

Sampling-based motion planners (e.g., RRT*, IRRT*) suffer from slow convergence, poor initial solution quality, high solution variance, and insufficient robustness in complex 2D environments. To address these limitations, this paper proposes Structural-Aware Enhanced SIRRT*, a novel variant of the SIRRT* algorithm. Its key contributions are: (1) skeleton-driven deterministic initialization—first introduced to drastically reduce time-to-first-path; (2) a hybrid path smoothing strategy integrating B-spline fitting with collision-aware correction; and (3) a local bidirectional tree rerouting mechanism to accelerate cost propagation and global optimization. Evaluated over 100 benchmark trials, the proposed method improves initial path quality by 32%, accelerates convergence by 2.1×, and reduces solution variance by 76% compared to both IRRT* and the baseline SIRRT*. These advances significantly enhance planning robustness and reproducibility.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Improves slow convergence in RRT* path planning
• Enhances path quality with hybrid smoothing
• Reduces variance via bidirectional rewiring

Innovation

• Hybrid path smoothing with spline fitting
• Bidirectional rewiring for tree connectivity
• Deterministic skeleton-based initialization