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Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Published online by Cambridge University Press:  06 December 2023

Shangjunnan Liu Open the ORCID record for Shangjunnan Liu [Opens in a new window] ,
Shuhai Liu  and
Huaping Xiao
Shangjunnan Liu
Affiliation:
College of Mechanical and Storage and Transportation Engineering, China University of Petroleum-Beijing, Beijing, 102249, China
Shuhai Liu*
Affiliation:
College of Mechanical and Storage and Transportation Engineering, China University of Petroleum-Beijing, Beijing, 102249, China Center of Advanced Oil and Gas Equipment, China University of Petroleum-Beijing, Beijing, 102249, China
Huaping Xiao
Affiliation:
College of Mechanical and Storage and Transportation Engineering, China University of Petroleum-Beijing, Beijing, 102249, China Center of Advanced Oil and Gas Equipment, China University of Petroleum-Beijing, Beijing, 102249, China
*
Corresponding author: Shuhai Liu; Email: liu_shu_hai@163.com
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Abstract

With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC1 and AC2 of two different fitness functions, Fitness1 and Fitness2, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

Keywords

mobile charging robotspath planningGWOA* algorithmparking lots

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 2 , February 2024 , pp. 536 - 559
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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