文章摘要
秦珅,曹冲振,李赫,鹿国庆,王洪祥.基于双向同步A*算法的AGV路径规划仿真研究[J].包装工程,2019,40(21):159-166.
QIN Shen,CAO Chong-zhen,LI He,LU Guo-qing,WANG Hong-xiang.Simulation Research on AGV Path Planning Based on Bidirectional Synchronous A* Algorithm[J].Packaging Engineering,2019,40(21):159-166.
基于双向同步A*算法的AGV路径规划仿真研究
Simulation Research on AGV Path Planning Based on Bidirectional Synchronous A* Algorithm
投稿时间:2019-03-25  修订日期:2019-11-10
DOI:10.19554/j.cnki.1001-3563.2019.21.023
中文关键词: 双向同步A*算法  启发函数  路径最优  出行复杂度
英文关键词: bidirectional synchronization A* algorithm  heuristic function  path optimization  travel complexity
基金项目:
作者单位
秦珅 1.山东科技大学 交通学院山东 青岛 266590 
曹冲振 1.山东科技大学 交通学院山东 青岛 266590 
李赫 1.山东科技大学 交通学院山东 青岛 266590 
鹿国庆 1.山东科技大学 交通学院山东 青岛 266590 
王洪祥 2.山东科技大学 机械电子工程学院山东 青岛 266590 
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中文摘要:
      目的 解决A*算法在搜寻过程中查询的节点非常多、耗时较多等问题。方法 通过对传统A*算法进行改进,得到改进双向同步A*算法,使算法从起点和终点同时搜索路径,并且对启发函数进行改进。为验证改进算法效果,以仓储AGV为例进行二维与三维仿真。结果 改进双向同步A*算法在不同的地图下,耗时分别减少了39.1%,34.3%,34.6%,搜索节点最多减少了140,路径长度基本一致。结论 改进双向同步A*算法较传统A*算法能够更加高效地规划出最优路径,兼顾路径最优和出行复杂度低,提高了检索效率,能够有效解决查询节点多、耗时多等问题。
英文摘要:
      The paper aims to solve the problems of A* algorithm, such as a large number of nodes and time-consuming in search. The traditional A* algorithm and the bidirectional synchronization A* algorithm were improved, so that the algorithm can search the path from the starting point and the end point simultaneously, and improve the heuristic function. In order to verify the effectiveness of the improved algorithm, two-dimensional and three-dimensional simulation of warehouse AGV was carried out. The improved bidirectional synchronization A* algorithm reduced the time-consuming by 39.1%, 34.3%, 34.6% under different maps. The searching nodes can be reduced by 140 at most, and the path length was basically the same. The improved bidirectional synchronization A* algorithm is more efficient than the traditional A* algorithm in planning the optimal path, considering both the optimal path and the low travel complexity, improving the retrieval efficiency, and effectively solving the problems of more query nodes and more time-consuming.
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