改进的蚁群算法在智能车辆路径规划中的运用  

Application of Improved Ant Colony Algorithm in Intelligent Vehicles Path Planning

作　　者：蓝丹[1] 樊东红[2] 陈强 危维 LAN Dan;FAN Dong-hong;CHEN Qiang;WEI Wei(College of Automotive and Information Engineering,Guangxi Eco-engineering Vocational and Technical College,Liuzhou Guangxi 545004,China;College of Mathematics,Physics and Electronic Engineering,Guangxi Normal University for Nationalities,Chongzuo Guangxi 532200,China;不详)

机构地区：[1]广西生态工程职业技术学院汽车与信息工程学院,广西柳州545004 [2]广西民族师范学院数理与电子信息工程学院,广西崇左532200 [3]柳州电信分公司,广西柳州545004

出　　处：《组合机床与自动化加工技术》2021年第4期130-133,138,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：广西职业教育教学改革研究项目(GXGZJG2018A017)。

摘　　要：针对智能车辆路径规划问题,提出一种改进蚁群智能路径规划方法。首先,分析了传统蚁群算法的基本原理及存在的缺陷;其次,通过引入障碍物位置信息、改变启发式因子以及信息素更新方式,提出一种改进蚁群算法;最后,充分考虑智能车辆动力学约束,进一步对规划路径进行平滑和优化处理。采用栅格地图,建立智能车、车道以及动态障碍仿真场景进行车辆避障仿真实验,在存在静态和动态障碍物的不同道路环境下,均成功规划出一条路径更短且更为平滑的全局安全路径。实验结果表明所提方法能够较好地克服传统蚁群算法存在的缺陷,在提高算法效率的同时优化车辆行驶路径。For the problem of intelligent vehicle path planning,an improved ant colony intelligent path planning method is proposed.Firstly,the basic principle and defects of traditional ant colony algorithm are analyzed.Secondly,an improved ant colony algorithm is proposed by introducing obstacle location information,changing heuristic factors and pheromone updating methods.Finally,the dynamic constraints of the intelligent vehicle are fully considered to further smooth and optimize the planning path,The grid map is used to build intelligent vehicle,lane and dynamic obstacle simulation scene for vehicle obstacle avoidance simulation experiment.In different road environments with static and dynamic obstacles,a shorter and smoother safety path is successfully planned.The experimental results show that the proposed method can overcome the shortcomings of the traditional ant colony algorith,improve the efficiency of the algorithm and optimize the vehicle path.

关 键 词：智能车辆 动态避障 改进蚁群算法 栅格地图 

分 类 号：TH162[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]