汽车工程学报2024，Vol.14Issue(2)：193-204,12.DOI:10.3969/j.issn.2095‒1469.2024.02.04
基于高精地图的物流配送车路径规划与跟踪控制
High-Definition Map-Based Route Planning and Tracking Control for Logistics Distribution Vehicles
摘要
Abstract
To address the challenge of multi-waypoint delivery by unmanned vehicles in scenarios such as industrial parks,the paper proposes a lane-level global path planning,generation and tracking control method based on vectorized high-definition maps.Considering the influence of delivery point sequencing on the total path length,the A* algorithm is used based on high-definition maps to calculate the optimal path between each delivery point.And then,the dynamic programming algorithm is employed to obtain the globally optimal path that passes through multiple delivery points.The planned path is smoothed using Bezier curves,and the reference driving speed is set according to the road curvature at different points along the path,thereby creating a lane-level target trajectory suitable for tracking.Subsequently,a model predictive controller based on a two-degree-of-freedom vehicle model is designed for trajectory tracking to achieve autonomous control of low-speed logistics delivery vehicles.The proposed planning and control method is tested on a joint simulation platform of CarSim,PreScan and Simulink,as well as on a real vehicle platform.The results show,compared with the traditional path determined based on the nearest delivery point strategy,that the path length determined by the proposed method is reduced by an average of 6.15%.The developed trajectory tracking controller ensures that the lateral deviation of the experimental delivery vehicle from the target trajectory is maintained within 0.25 m and the yaw angle deviation is kept within 5°.关键词
物流工程/高精地图/全局路径规划/模型预测控制/末端配送Key words
logistics engineering/HD maps/global path planning/model predictive control/end-of-line distribution分类
交通工程引用本文复制引用
朱波,谈笑昊,谈东奎,胡旭东..基于高精地图的物流配送车路径规划与跟踪控制[J].汽车工程学报,2024,14(2):193-204,12.基金项目
国家重点研发计划项目(2018YFB0105102) （2018YFB0105102）
安徽省自然科学基金项目(2208085QE153) （2208085QE153）
安徽省科技重大专项(202004b11020002) （202004b11020002）
