改进的RRT*算法，路径更优且收敛更快
Enhanced SIRRT*: A Structure-Aware RRT* for 2D Path Planning with Hybrid Smoothing and Bidirectional Rewiring
- 基于结构感知初始化与混合平滑优化路径
- 100次实验中路径质量、收敛速度均优于原有方法
- 适合对路径稳定性要求高的机器人导航场景
基于采样的运动规划器如RRT*及其改进版本IRRT*广泛应用于复杂环境中的最优路径规划。然而，这些方法因依赖随机采样，常出现收敛慢、结果波动大的问题，尤其在初始解发现延迟时更为明显。本文提出增强型SIRRT*（E-SIRRT*），在原SIRRT*框架基础上引入两项关键改进：混合路径平滑与双向重连机制。混合平滑通过样条拟合与碰撞感知修正提升初始路径质量；双向重连则在平滑路径周围局部优化树的连接关系，改善代价传播效率。实验表明，E-SIRRT*在100次测试中持续优于IRRT*和SIRRT*，在初始路径质量、收敛速率与鲁棒性方面表现更佳。相较于依赖随机初始化而波动较大的IRRT*，E-SIRRT*通过确定性骨架初始化与结构化优化，实现可重复、高效的性能表现。
原文摘要 · Abstract (English)
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.
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