基于混合A*算法的无人驾驶矿用卡车路径优化研究  

Research on Hybrid A*Based Path Optimization of Unmanned Mine Truck

作　　者：邓穆坤 刘勇 黄佳德 罗羽 DENG Mukun;LIU Yong;HUANG Jiade;LUO Yu(Zhuzhou CRRC Times Electric Co.,Ltd.,Zhuzhou,Hunan 412001,China)

机构地区：[1]株洲中车时代电气股份有限公司,湖南株洲412001

出　　处：《控制与信息技术》2022年第5期60-67,共8页CONTROL AND INFORMATION TECHNOLOGY

基　　金：国家重点研发计划(2021YFB2501802)。

摘　　要：为提高混合A*算法对矿区场景的适应性,文章提出一种无人驾驶矿用卡车(简称“矿卡”)路径优化方法。其首先结合矿区场景进行CCRS曲线筛选以提高混合A*算法初始解的适用性和曲率的连续性,然后采用基于二次规划的分段样条曲线数值优化方法对搜索得到的路径进行进一步的优化平滑及插值。该方法可应用于路径规划初始解的筛选及平滑优化,为无人驾驶矿卡提供一条安全、平滑且满足车辆运动学约束的可行驶路径。仿真对比实验与实车试验结果表明,所提方法能显著提高传统混合A*算法搜索路径的适用性、平滑性,且平滑效率相较于传统离散点平滑方法平均提高10倍。In order to improve the adaptability of hybrid A*algorithm to mining scene, this paper proposes a path optimization method for unmanned mine trucks. Firstly, applicability and curvature continuity of initial solution of hybrid A* algorithm are improved by curvature continuous Reeds-Shepp screening in combination with mining scenes, and then the segmented spline curve numerical optimization method based on quadratic programming is used to further smooth and interpolate the searched path. This method can be applied to the screening and smooth optimization of the initial solution of path planning, and provides a safe, smooth and drivable path for unmanned mine trucks that satisfies the kinematic constraints of the vehicle. The results of simulation comparison test and on site vehicle test show that the proposed method can significantly improve the applicability and smoothness of the search path by the traditional hybrid A* algorithm, and the optimization efficiency is better than the traditional discrete point optimization.

关 键 词：无人驾驶 矿用卡车 路径平滑 曲率连续Reeds-Shepp(CCRS) 分层筛选 二次规划 分段样条曲线 

分 类 号：TD67[矿业工程—矿山机电]