基于修正PRM算法的智能车辆路径规划研究  

Smart Vehicle Path Planning Based on Modified PRM Algorithm

作　　者：李琼琼 徐溢琪 布升强 杨家富[1] 陈勇[1] LI Qiongqiong;XU Yiqi;BU Shengqiang;YANG Jiafu;CHEN Yong(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China;Hundsun Technologies Inc,Hangzhou 310053,China)

机构地区：[1]南京林业大学机械电子工程学院,南京210037 [2]恒生电子股份电子有限公司,杭州310053

出　　处：《森林工程》2022年第5期179-186,共8页Forest Engineering

基　　金：国家自然科学基金项目(32072498);南京市科技创新项目(2015CG047)。

摘　　要：针对概率地图算法(Probabilistic Road Map, PRM)存在选取采样点缺乏导向性、路标图复用率低和算法搜索效率低等问题,设计一种以空间主轴线为参照轴的伪随机采样策略,优化采样点的生成方式,去除冗余采样点;设置路点之间距离阈值,采用双向递增方法进行碰撞检测,优化碰撞检测调用次数,提取规划路径的关键路点作为贝塞尔曲线的离散控制点,对路径进行平滑处理,使生成的路径更加符合车辆的行驶工况,利用Matlab、ROS搭建试验平台对修正PRM算法的正确性进行验证分析;对比基本PRM算法,修正PRM算法在路标图的构建速度、路径规划速率和路径长度3方面具有明显的优势。Aiming at the problems of lack of guidance in selecting sampling points, low reuse rate of road signs and low search efficiency of probabilistic map algorithm(PRM), a pseudo-random sampling strategy based on the spatial main axis as the reference axis was designed, which optimized the generation mode of sampling points and removed redundant sampling points. The distance threshold between road points was set, and the two-way incremental method was used for collision detection, which optimized the call times of collision detection. The key points of the planned path were extracted as the discrete control points of the Bezier curve, and the path was smoothed to make the generated path more in line with the driving conditions of the vehicle. MATLAB and ROS were used to build a test platform to verify and analyze the correctness of the modified PRM algorithm. Compared with the basic PRM algorithm, the modified PRM algorithm had obvious advantages in the construction speed of road map, path planning speed and path length.

关 键 词：智能车辆 修正概率地图算法 伪随机采样 碰撞检测 路径规划 

分 类 号：S762[农业科学—森林保护学] TP399.9[农业科学—林学]