[1]顾文斌,陈泽宇,吴亚伟,等.融合栅格地图模型的改进 AGV 路径规划算法研究[J].计算机技术与发展,2021,31(09):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 001]
　GU Wen-bin,CHEN Ze-yu,WU Ya-wei,et al.Research on Improved AGV Path Planning Algorithm Based on Grid Map Model[J].,2021,31(09):1-6.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 001]
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融合栅格地图模型的改进 AGV 路径规划算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年09期
页码:
1-6
栏目:
人工智能
出版日期:
2021-09-10

文章信息/Info

Title:
Research on Improved AGV Path Planning Algorithm Based on Grid Map Model
文章编号:
1673-629X(2021)09-0001-06
作者:
顾文斌1 ; 陈泽宇1 ; 吴亚伟2 ; 苑明海1
1. 河海大学 机电学院,江苏 常州 213022
2. 常州工程职业技术学院,江苏 常州 213022
Author(s):
GU Wen-bin1 ; CHEN Ze-yu1 ; WU Ya-wei2 ; YUAN Ming-hai1
1. School of Mechanical and Electrical Engineering,Hohai University,Changzhou 213022,China
2. Changzhou Vocational Institute of Engineering,Changzhou 213022,China
关键词:
自动引导小车; 路径规划; 蚁群算法; 蜂巢栅格模型; 信息素更新
Keywords:
automated guided vehicle; path planning; ant colony algorithm; honeycomb-grid-model; pheromones updating
分类号:
TP18;TH16
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 09. 001
摘要:
在传统自动引导小车(automated guided vehicle,AGV)路径规划研究方法中,针对环境模型多为正方形栅格导致模拟效果差的问题,提出了一种基于蜂巢栅格形状的地图模型,并针对传统蚁群算法求解路径规划问题时效率低下且结果不稳定的缺点,提出了一种基于改进型蚁群算法的 AGV 路径规划方法。 首先,利用蜂巢栅格对环境进行建模,再使用改进型蚁群算法,根据每只蚂蚁和每次迭代的评估,使用不同的信息素更新规则来得到最终路径。 实验结果表明,改进型蚁群算法解决了传统蚁群算法不能较好收敛的问题,并能获得更短的规划路径。 再和相关文献算法的结果进行对比,发现使用改进型蚁群算法能在算法前期获得更好的路径采集效果,在算法后期能获得更好的收敛效果,提高了路径搜索的准确性和稳定性。
Abstract:
In the traditional research methods of automated guided vehicle ( AGV ) path planning, aiming at the problem that the environment model is mostly square grid,which leads to poor simulation effect,a map model based on the shape of honeycomb grid is proposed. Aiming at the shortcomings of low efficiency and unstable results? of traditional ant colony algorithm,an AGV path planning method based on improved ant colony algorithm is proposed.? Firstly,the environment is modeled by using the honeycomb grid,and then the improved ant colony algorithm is used to obtain the final path according to the evaluation of each ant and each iteration. The experiment shows that the improved ant colony algorithm can solve the problem that the traditional ant colony algorithm can not converge well,and can obtain shorter planning path. Compared with the results of related literature algorithms,it is found that the improved ant colony algorithm can obtain better path acquisition effect in the early stage of the algorithm,and better convergence effect in the later stage of the algorithm,so as to improve the accuracy and stability of path search.

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更新日期/Last Update: 2021-09-10