基于Maklink路径规划混合定位算法研究  

Research on hybrid location algorithm based on Maklink path planning

作　　者：杨俊磊 段倩倩 YANG Junlei;DUAN Qianqian(School of Electronic and Electrical Engineering,Shanghai University of Engineering and Technology,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《传感器与微系统》2022年第2期62-65,共4页Transducer and Microsystem Technologies

基　　金：上海市青年科技英才扬帆计划项目(17YF1428100)。

摘　　要：为了提高含有障碍物下的路径规划的质量和效率,保证遍历节点少且路径短,提出了一种混合定位算法。算法融合了粒子群算法选择最佳位置的优越性,以提高蚁群算法蚂蚁所在节点与盲节点间距离估计的精度和搜寻速度;根据蚁群算法通过信息素浓度选择遍历节点的特点,规划节点与盲节点之间的最短距离,以增强蚁群信息素的浓度值,提高收敛速度,规划最短路径。实验表明:在不同规模障碍物区域路径搜寻过程中,采用混合定位算法规划路径优于文中其他两种算法,规划路径最短,效率最高。In order to improve the quality and efficiency of path planning with obstacles, and to ensure less traversal nodes and short path, a hybrid location algorithm is proposed.The algorithm combines the advantages of particle swarm optimization algorithm to select the best position, so as to improve the accuracy and search speed of distance estimation between the ant’s node and the blind node;according to the characteristics of ant colony algorithm to select the traversal node through the pheromone concentration, the shortest distance between the node and the blind node is planned to enhance the pheromone concentration value of ant colony, improve the convergence speed and plan the shortest path.The experiment shows that the hybrid location algorithm is better than the other two algorithms in the path searching process of different scale obstacles, and the shortest path with the highest efficiency.

关 键 词：路径规划 混合定位 Maklink图 信息素 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]