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Path planning of patrol robot based on modified grey wolf optimizer

Published online by Cambridge University Press:  13 March 2023

Qian Zhang Open the ORCID record for Qian Zhang [Opens in a new window] ,
Xucheng Ning ,
Yingying Li Open the ORCID record for Yingying Li [Opens in a new window] ,
Lei Pan ,
Rui Gao  and
Liyang Zhang
Qian Zhang
Affiliation:
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China
Xucheng Ning
Affiliation:
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China
Yingying Li*
Affiliation:
Tianjin Research Institute of Construction Machinery, Tianjin, 300409, China
Lei Pan
Affiliation:
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China
Rui Gao
Affiliation:
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China
Liyang Zhang
Affiliation:
School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, 300384, China
*
*Corresponding author. E-mail: liyingyingscholar@163.com
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Abstract

The grey wolf optimizer (GWO) as a new intelligent optimization algorithm has been successfully applied in many fields because of its simple structure, few adjustment parameters and easy implementation. This paper mainly aims at the defects of GWO in path planning application, such as easily falling into local optimization, poor convergence and poor accuracy, and turn point grey wolf optimization (TPGWO) algorithm is proposed. First, the idea of cross-mutation and roulette is used to increase the initial population of GWO and improve the search range. At the same time, the convergence factor function is improved to become a nonlinear update. In the early stage, the search range is expanded, and in the later stage, the convergence speed is increased, while the parameters in the convergence factor function can be adjusted according to the number of obstacles and the map area to change the turning point of the function to improve the convergence speed and accuracy of the algorithm. The turning times and turning angles of the obtained path are added to the fitness function as penalty values to improve the path accuracy. The optimization test is carried out through 16 test functions, and the test results prove the convergence and robustness of TPGWO algorithm. Finally, the TPGWO algorithm is applied to the path planning of patrol robot for simulation experiments. Compared with the GWO algorithm and Particle Swarm Optimization, the simulation results show that the TPGWO algorithm has better convergence, stability and accuracy in the path planning of patrol robot.

Keywords

path planningmotion planningmobile robotspatrol robotgrey wolf optimizer
Type
Research Article
Information
Robotica , Volume 41 , Issue 7 , July 2023 , pp. 1947 - 1975
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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