提出加速机器人路径规划算法，速度提升15倍且精度不变
Accelerated Reeds-Shepp and Under-Specified Reeds-Shepp Algorithms for Mobile Robot Path Planning
- 用几何分析重构状态空间，减少候选路径数量
- 相比原版C++实现快15倍，路径长度误差在机器精度内
- 开源实现支持未指定终点朝向的规划场景
本文提出一种简单直观的方法，加速最优Reeds-Shepp路径计算。通过几何推理分析最优路径行为，实现新的状态空间划分，并进一步缩小可行路径集。我们重实现经典方法以提供基准对比，这些方法此前缺乏现代开源实现。此外，解决终点方向未指定的受约束规划问题。通过全面实验验证，新方法相较Open Motion Planning Library中现代C++实现提速15倍，经典方法提速5.79倍。两者路径长度与原解相比均保持机器精度差异。我们已将加速及未指定方向的Reeds-Shepp算法C++实现开源发布。
原文摘要 · Abstract (English)
In this study, we present a simple and intuitive method for accelerating optimal Reeds-Shepp path computation. Our approach uses geometrical reasoning to analyze the behavior of optimal paths, resulting in a new partitioning of the state space and a further reduction in the minimal set of viable paths. We revisit and reimplement classic methodologies from the literature, which lack contemporary open-source implementations, to serve as benchmarks for evaluating our method. Additionally, we address the under-specified Reeds-Shepp planning problem where the final orientation is unspecified. We perform exhaustive experiments to validate our solutions. Compared to the modern C++ implementation of the original Reeds-Shepp solution in the Open Motion Planning Library, our method demonstrates a 15x speedup, while classic methods achieve a 5.79x speedup. Both approaches exhibit machine-precision differences in path lengths compared to the original solution. We release our proposed C++ implementations for both the accelerated and under-specified Reeds-Shepp problems as open-source code.
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