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Hierarchical Path Planning Method for Mobile Robots Based on Hybrid Genetic Particle Swarm Optimization Algorithm
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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41 Number of periods: 2020 04 Page number: 34-40 Column: Public date: 2020-08-12
Title:
Hierarchical Path Planning Method for Mobile Robots Based on Hybrid Genetic Particle Swarm Optimization Algorithm
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
Keywords:
mobile robot; path planning; artificial potential field method; improved genetic algorithm; particle swarm optimization algorithm; triangle method
CLC:
-
DOI:
10.13705/j.issn.1671-6833.2020.01.011
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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