[1]欧阳海滨,全永彬,高立群,等.基于混合遗传粒子群优化算法的层次路径规划方法[J].郑州大学学报(工学版),2020,41(04):34-40.[doi:10.13705/j.issn.1671-6833.2020.01.011]
 Ouyang Seashore,Jeon Yong BinGao LiqunZou Dexuan.SI5 Global hierarchical path planning of mobile robot based on hybrid genetic particle swarm optimization algorithm[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):34-40.[doi:10.13705/j.issn.1671-6833.2020.01.011]
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基于混合遗传粒子群优化算法的层次路径规划方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年04期
页码:
34-40
栏目:
出版日期:
2020-08-12

文章信息/Info

Title:
SI5 Global hierarchical path planning of mobile robot based on hybrid genetic particle swarm optimization algorithm
作者:
欧阳海滨全永彬高立群邹德旋
1. 广州大学机械与电气工程学院2. 东北大学信息科学与工程学院3. 江苏师范大学电气工程及自动化学院
Author(s):
Ouyang Seashore1Jeon Yong Bin1Gao Liqun2Zou Dexuan3
1. School of Mechanical and Electrical Engineering, Guangzhou University2. School of Information Science and Engineering, Northeastern University3. School of Electrical Engineering and Automation, Jiangsu Normal University
关键词:
移动机器人' target="_blank" rel="external">">移动机器人路径规划人工势场法改进遗传算法粒子群优化算法三角形法
Keywords:
mobile robots' target="_blank" rel="external">">mobile robotspath planningartificial potential field methodimproving genetic algorithmsparticle swarm optimization algorithmtriangle method
DOI:
10.13705/j.issn.1671-6833.2020.01.011
文献标志码:
A
摘要:
路径规划是移动机器人研究领域的一个重要基础性问题。单独使用某一路径规划算法存在着或搜索速度慢,或易陷入局部极值的缺陷。为解决这些不足,提出了一种基于混合遗传粒子群优化算法的层次路径规划方法。该方法的主要内容包括:一是基于三角形法的空间环境路径建模;二是结合人工势场法的改进遗传算法设计及初次路径规划;三是运用粒子群优化算法对初次路径规划的结果进行优化以实现更可靠的最优路径。通过一些实例仿真测试,结果显示所设计的方法能够融合各算法的优点,快速有效地找到最优路径。
Abstract:
Path planning is an important basic task in the field of mobile robot research. Using a certain path planning algorithm alone has the defect of search at slow speed and is easy to fall into local extreme value. In order to solve these problems, a global hierarchical path planning method based on time series is proposed in this paper, which combines the advantages of each algorithm. There are three main aspects of this method. First, we build the spatial environment based on the triangle method. Second, we design an improved genetic algorithm combined with artificial potential field for initial path planning. Third, we use particle swarm optimization algorithm to optimize the results of the initial path planning to achieve a more reliable optimal path. The simulation results show that the method can integrate the advantages of each algorithm and find the optimal path quick and efficient

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更新日期/Last Update: 2020-10-06