使用改进蚁群算法的AGV路径规划研究  

Research on Path Planning for AGV Based on Improved Ant Colony Algorithm

作　　者：葛志远 肖本贤[1] GE Zhi-yuan;XIAO Ben-xian(School of Electrical Engineering and Automation,Hefei University of Technology,Anhui Hefei230009,China)

机构地区：[1]合肥工业大学电气与自动化工程学院,安徽合肥230009

出　　处：《机械设计与制造》2020年第6期241-244,248,共5页Machinery Design & Manufacture

摘　　要：AGV路径规划问题是AGV研究领域的一个关键技术问题.针对传统的蚁群算法耗时长,搜索效率低,容易出现次优的缺点,改进了计算基本蚁群算法启发因子的方法;提出了优胜劣汰机制以及全局信息素调整方案,合理地更新了路径规划中的信息素;利用最大最小蚂蚁系统对路径上信息素进行了限制;研究了路径规划中死锁问题的解决方法.最后给出了基于改进蚁群算法的AGV路径规划步骤并进行了仿真实验.仿真实验结果表明,在该算法作用下,AGV路径规划的搜索效率优于传统蚁群算法,且规划路径更短,提高了搜索的准确性.AGV path planning is a key technical issue in AGV research.In view of the disadvantages of the traditional ant colony algorithm,such as time-consuming,low search efficiency and sub-optimal disadvantages.We improve the method to calculate the heuristic factor of basic ant colony algorithm.Global pheromone adjustment program,which reasonably updates the pheromone in the path planning;limits the pheromone on the path by using the maximum and minimum ant system;and studies the solution to the deadlock problem in path planning.Finally,the AGV path planning step based on improved ant colony algorithm is given and the simulation experiment is carried out.The simulation results show that under the action of the algorithm,the search efficiency of AGV path planning is better than that of the traditional ant colony algorithm,and the planning path is shorter,which improves the accuracy of search.

关 键 词：路径规划 AGV 蚁群算法 信息素 最大最小蚂蚁系统 死锁现象 

分 类 号：TH16[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]