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基于改进 PRM 算法的智能车辆路径规划。

Smart Vehicle Path Planning Based on Modified PRM Algorithm.

机构信息

College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

出版信息

Sensors (Basel). 2022 Aug 31;22(17):6581. doi: 10.3390/s22176581.

DOI:10.3390/s22176581
PMID:36081038
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9460667/
Abstract

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.

摘要

路径规划是移动智能车辆在复杂环境中非常重要的一步。基于采样的规划方法，如概率路标法 (PRM)，已经被广泛应用于智能车辆中。然而，它存在一些缺点，例如效率低、路标再利用率低以及采样点选择缺乏指导。为了解决上述问题，我们设计了一种以主空间轴为参考轴的伪随机采样策略。我们优化了采样点的生成，去除了冗余的采样点，设置了路标之间的距离阈值，采用了双向增量方法进行碰撞检测，并优化了碰撞检测调用的次数，以提高路标构建的效率。将规划路径的关键道路点提取为贝塞尔曲线的离散控制点，并对路径进行平滑处理，使生成的路径更符合车辆的行驶条件。使用 MATLAB 和 ROS 构建测试平台验证和分析了改进后的 PRM 的正确性。与基本 PRM 算法相比，改进后的 PRM 算法在构建路标、路径规划和路径长度方面具有速度优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc40/9460667/814e1a8cff0e/sensors-22-06581-g015.jpg
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