基于双层蚁群算法和轨迹优化的移动机器人导航算法  

MOBILE ROBOT NAVIGATION ALGORITHM BASED ON DOUBLE-LAYER ANT COLONY ALGORITHM AND TRAJECTORY OPTIMIZATION

作　　者：郝延春 Hao Yanchun(College of Electronic Science and Engineering,Jilin University,Changchun 130012,Jilin,China;Quality Control Department,Jilin Railway Vocational and Technical College,Jilin 132200,Jilin,China)

机构地区：[1]吉林大学电子科学与工程学院,吉林长春130012 [2]吉林铁道职业技术学院质量监控处,吉林吉林132200

出　　处：《计算机应用与软件》2022年第6期239-245,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(U19A2069)。

摘　　要：针对蚁群算法在路径规划中存在收敛速度慢、易陷入局部最优的问题,提出一种双层蚁群优化算法(DL-ACO)。提出一种并行蚁群优化方法,在复杂地图中生成初始无碰撞路径;采用拐点优化算法(TPOA),对初始路径的长度、转折点数进行优化。为了便于拐角处移动机器人的跟踪控制,设计分段B样条路径平滑器,使拐角处更为平滑。仿真实验表明,所提出的DL-ACO算法在路径长度、平滑度和算法稳定性上均优于其他几种算法;场景实验表明,DL-ACO算法能够有效应用于移动机器人的路径规划。Ant colony algorithm has slow convergence and is easy to fall into local optimum for path planning.Therefore,we propose a double-layer ant colony optimization algorithm(DL-ACO).We put forward a parallel ant colony optimization method to generate an initial collision-free path in a complex map.We used the turning point optimization algorithm(TPOA)to optimize the length of the initial path and the number of turning points.To facilitate the tracking control of mobile robots at the corners,a segmented B-spline path smoother was designed to make the corners smoother.Simulation experiments show that the proposed DL-ACO is superior to several other algorithms in path length,smoothness,and algorithm stability.Scene experiments show that DL-ACO can be effectively applied to mobile robot path planning.

关 键 词：路径规划 并行蚁群算法 移动机器人 DL-ACO 分段B样条 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]