基于改进A^(*)和改进DWA相融合的AGV路径规划  

Improved A^(*) algorithm fused with improved DWA for AGV Path Planning

作　　者：温颖 王冬梅[1] 路玉麟 WEN Ying;WANG Dongmei;LU Yulin(School of Automotive and Traffic Engineering,Liaoning University of Technology,Jinzhou 121001,Liaoning,China)

机构地区：[1]辽宁工业大学汽车与交通工程学院,辽宁锦州121001

出　　处：《农业装备与车辆工程》2024年第9期123-127,共5页Agricultural Equipment & Vehicle Engineering

摘　　要：为解决A^(*)算法对无人导引车(AGV)路径规划时存在的寻路时间长、路径不平滑等问题,提出一种改进A^(*)算法与改进DWA相融合的算法。将对数衰减函数同幂函数结合作为动态权重系数引入A^(*)算法,以此调节搜索的偏向性;设计了一种路径节点平滑处理算法以减少路径中的转折点。在DWA算法的评价函数中引入全局距离评价函数,使局部路径贴合全局路径;在加权系数的调控中引入双曲正切函数,使其自动调节分配权重因子。对融合改进后的两种算法进行仿真实验,结果表明,优化后的算法在平滑度、路径长度、搜索效率等方面都有明显改善。In order to solve the problems of long pathfinding time and unsmooth path in the path planning of automated guided vehicle(AGV) by the A^(∗) algorithm,an algorithm combining improved A^(∗) and improved DWA was proposed.Firstly,the combination of the logarithmic decay function and the power function was introduced into the A^(∗) algorithm as a dynamic weight coefficient to adjust the bias of the search.Then,a path node smoothing algorithm was designed to reduce the turning points in the path.Additionally,the global distance evaluation function was introduced into the evaluation function of the DWA algorithm to make the local path conform to the global path,and the hyperbolic tangent function was introduced into the adjustment of the weighting coefficient to automatically adjust the allocation weight factor.Finally,the simulation experiments were carried out by fusing the two improved algorithms,and the results showed that the optimized algorithm had significant improvements in smoothness,path length,search efficiency and other aspects.

关 键 词：无人导引车 路径规划 改进A^(*)算法 DWA算法 避障 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP23[自动化与计算机技术—控制科学与工程]