非结构化场景下的无人车轨迹规划研究  

Study on Unmanned Vehicle Trajectory Planning in Unstructured Scenarios

作　　者：朱威[1] 杨世博 滕帆 何德峰[1] ZHU Wei;YANG Shibo;TENG Fan;HE Defeng(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《计算机科学》2024年第4期334-343,共10页Computer Science

基　　金：浙江省自然科学基金(LY21F010009);国家自然科学基金(62173303)。

摘　　要：针对传统无人车轨迹规划算法在非结构化场景下存在实时性较低和轨迹平滑性较差等问题,提出了一种前后端分离的轨迹规划算法。该算法的前端路径搜索部分对Hybrid A*算法在控制空间进行搜索范围的剪枝且保留了车辆的运动学约束,并通过优化启发函数的计算方式,提高了图搜索的实时性。该算法的后端轨迹优化部分分为两个阶段:第一阶段设计了一个软约束非线性多目标优化器对路径进行局部优化,生成离散的轨迹位姿点和时间分配值;第二阶段基于五次样条曲线利用最小化Jerk的思想对离散位姿点进行平滑连接,提高了轨迹的平滑性。最后在室外停车场环境下对所提算法进行了实车测试,前端路径搜索和后端轨迹优化的实验结果表明该算法具有较高的实时性和轨迹平滑性。Aiming at the problems of low real-time performance and poor track smoothness of traditional unmanned vehicle tra-jectory planning algorithm in unstructured scenes,this paper proposes a front and rear separated trajectory planning algorithm.The front-end path search part of the algorithm prunes the search range of the Hybrid A*algorithm in the control space and retains the kinematic constraints of the vehicle,and improves the real-time performance of the graph search by optimizing the calculation method of the heuristic function.The back-end trajectory optimization part of the algorithm is divided into two stages:in the first stage,a soft-constrained nonlinear multi-objective optimizer is designed to locally optimize the path and generate discrete trajectory pose points and time allocation values;in the second stage,based on the quintic spline uses the idea of minimizing Jerk to smoothly connect the discrete pose points,which improves the smoothness of the trajectory.Finally,the proposed algorithm is tested on a real vehicle in an outdoor parking lot environment.Experimental results of front-end path search and back-end trajectory optimization show that the algorithm has high real-time performance and trajectory smoothness.

关 键 词：轨迹规划 无人车 非结构化场景 多目标优化 样条曲线 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]