| 基于DBSCAN与B样条曲线的Informed RRT*路径规划方法 |
投稿时间：2024-05-11 修订日期：2024-06-05  点此下载全文 |
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| 中文摘要:智能车辆在多障碍物非结构化场景中，传统Informed RRT*算法存在计算效率低、路径平滑性差的问题。本文提出了一种基于密度聚类算法DBSCAN（density-based clustering）与三次B样条的Informed RRT*路径规划方法。首先，设计基于DBSCAN算法的障碍物聚类方法，简化多障碍物非结构化场景。然后，提出基于三次B样条的平顺性方法，使生成的路径平均曲率降低，路径平滑点增加。最后，基于MATLAB进行仿真，搜索出一条更平滑的路径。结果表明，在多障碍物非结构化场景中，本文提出的改进Informed RRT*算法相对传统算法效率提高了28.54%，路径平滑度由曲率0.14提升到0.12，取得显著效果。 |
| 中文关键词:DBSCAN 路径规划； Informed RRT*； B样条曲线； 路径平滑 |
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| Informed RRT* path planning method based on DBSCAN and B-spline curves |
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| Abstract:In the unstructured scenario of multiple obstacles, the traditional Informed RRT* algorithm has the problems of low computational efficiency and poor path smoothness. In this paper, we propose an Informed RRT* path planning method based on density-based clustering (DBSCAN) and cubic B-splines. Firstly, an obstacle clustering method based on DBSCAN algorithm was designed to simplify the unstructured scenario of multiple obstacles. Then, a smoothness method based on cubic B-spline is proposed, which reduces the average curvature of the generated path and increases the smoothness point of the path. Finally, the simulation based on MATLAB was carried out to find a smoother path. The results show that the improved Informed RRT* algorithm proposed in this paper is 17.15% more efficient than the traditional algorithm in the unstructured scenario with multiple obstacles, and the path smoothness increased from 0.14 to 0.12, which achieves remarkable results. |
| keywords:DBSCAN path planning Informed RRT* B-spline curve path smoothing |
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