[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 Haibin,QUAN Yongbin,GAO Liqun,et al.Hierarchical Path Planning Method for Mobile Robots 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
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:
Hierarchical Path Planning Method for Mobile Robots 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 Haibin1; QUAN Yongbin1; GAO Liqun2; ZOU Dexuan3
1.School of Mechanical and Electric Engineering,Guangzhou University,Guangzhou 510006,China;2.College of Information Science &Engineering,Northeastern University,Shenyang 110819,China;3.School of Electrical Engineering and Automation,Jiangsu Normal University,Xuzhou 221116,China
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 robot; path planning; artificial potential field method; improved genetic algorithm; particle swarm optimization algorithm; triangle 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 was an important basic problem in the research field of mobile robot.In order to solve the problem of slow searching speed or easily falling into local extremum when using a path planning algorithm alone,a hierarchical path planning method based on hybrid genetic particle swarm optimization (HGA-PSO) was proposed.The main contents of this method include the following parts.Firstly,the triangle method was used to model the space environment path.Secondly,combined with artificial potential field method,an improved genetic algorithm for initial path planning was designed.Thirdly,the particle swarm optimization algorithm was used to optimize the results of the initial path planning to achieve a more reliable optimal path.The simulation results show that the proposed method could 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