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基于聚类和融合算法的AGV路径搜索研究

Research on AGV Path Search Based on Clustering and Fusion Algorithm
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摘要 针对地图处理不当和货架碰撞的问题,设计了一种基于聚类和融合算法的自动导引车辆AGV路径搜索方法。聚类使用基于Canopy的K-means聚类算法,融合算法使用基于A*的改进蚁群算法和Bresenham直线算法,并分别应用于环境地图预处理和路径搜索。实验数据结果表明,提出的方法达到预期效果,为AGV的安全作业提供了高效、稳妥的最短避障路径选择。 In order to solve the problem of improper map handling and collision of shelves,an AGV path search method based on clustering and fusion algorithm was designed.The K-means clustering algorithm based on Canopy,and the improved ant colony algorithm based on A* and the Bresenham line algorithm were used in the fusion algorithm,and applied to the AGV production environment preprocessing and path search of the crowded shelves.Experimental results show that the proposed method achieves the expected effect and provides a selection of the shortest path for AGV obstacle avoidance.
作者 任彧 胡海荣 REN Yu;HU Hairong(School of Computer,Hangzhou Dianzi University,Hangzhou Zhejiang 310018,China)
出处 《杭州电子科技大学学报(自然科学版)》 2019年第1期39-44,共6页 Journal of Hangzhou Dianzi University:Natural Sciences
关键词 路径规划 聚类 融合算法 path planning clustering fusion algorithm
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