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

• Published in: Robotica Vol. 42; no. 2; pp. 536 - 559
• Main Authors: Liu, Shangjunnan, Liu, Shuhai, Xiao, Huaping
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.02.2024
• Subjects: Algorithms; Artificial intelligence; Automobiles; Autonomous navigation; Charging; Electric vehicles; Fitness; Heuristic; Local optimization; Minimum cost; Mold; Optimization techniques; Parking facilities; Path planning; Robots; A algorithm; mobile charging robots; parking lots; path planning; GWO
• ISSN: 0263-5747; 1469-8668
• DOI: 10.1017/S0263574723001625

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 AC 1 and AC 2 of two different fitness functions, Fitness 1 and Fitness 2, 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.