基于改进蚁群算法的AGV路径规划  

AGV Path Planning Based on Improved Ant Colony Algorithm

作　　者：丁林豪 蒋红琰 DING Linhao;JIANG Hongyan(School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Management,Xi'an Jiaotong University,Xi'an 710049,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044 [2]西安交通大学管理学院,西安710049

出　　处：《自动化与仪表》2025年第3期66-70,共5页Automation & Instrumentation

基　　金：科技部国家重点研发计划项目(YS2021YFE010470)。

摘　　要：针对在AGV路径规划的研究中,传统的蚁群算法常常因为迭代过程缓慢、易于陷入局部最优以及产生过多拐角等问题。为此,该文介绍了一种优化的蚁群算法,提升AGV路径规划的效率和效果。首先,通过指示期望方向的向量减少搜索的方向,避免不必要的计算;其次,通过多启发式函数融合的方式,引入了到起点和终点的距离、方向一致性和障碍物密度等多个影响因素,以提高算法的搜索效率和路径质量;再次,对信息素更新规则进行了改进,筛选符合条件的路径进行信息素更新,以加速算法收敛;最后,在每轮迭代结束后,对当前的最佳路径进行拉直化处理,以进一步提高路径质量。实验结果表明,改进的蚁群算法路径长度更短,收敛速度更快,且拐点次数更少。For the research in AGV path planning,the traditional ant colony algorithm is often due to the slow itera-tion process,easy to fall into the local optimum and produce too many corners.For this reason,this paper introduces an optimized ant colony algorithm to improve the efficiency and effectiveness of AGV path planning.First,the direc-tion of search is reduced by vectors indicating the desired direction to avoid unnecessary computation.Second,multi-ple influencing factors,such as distance to the start and end points,directional consistency,and obstacle density,are introduced by means of multi-heuristic function fusion to improve the search efficiency and path quality of the algo-rithm.Again,the pheromone updating rule is improved to screen the eligible paths for pheromone updating to accel-erate the convergence of the algorithm.Finally,at the end of each iteration,we straighten the current best path to further improve the path quality.The experimental results show that the improved ant colony algorithm has shorter path lengths,faster convergence,and fewer inflection points.

关 键 词：自动导引车 蚁群算法 路径规划 物流机器人 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]