A self-adaptive neighborhood search A-star algorithm for mobile robots global path planning

• Published in: Computers & electrical engineering Vol. 123; p. 110018
• Main Authors: Huang, Jiabo, Chen, Chunmei, Shen, Junjie, Liu, Guihua, Xu, Feng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2025
• Subjects: A-star algorithm; Mobile robot; Neighborhood search; Path planning; Path planning; Neighborhood search; A-star algorithm; Mobile robot
• ISSN: 0045-7906
• DOI: 10.1016/j.compeleceng.2024.110018

Aiming to address the issues of low search efficiency and unsmooth paths in traditional A-star algorithm for mobile robots global path planning, this paper proposes an improved A-star algorithm. Firstly, to tackle the inefficiency of 16-neighborhood search and the unsmooth paths of 8-neighborhood search, a new 5-neighborhood search method is introduced, which retains the path smoothness characteristics of 16-neighborhood search while achieving higher time efficiency. Secondly, an obstacle constraint approach using 4-neighborhood search is proposed to address the issues of crossing obstacles and getting trapped in dead ends encountered in 5-neighborhood search. Furthermore, the improved algorithm employs second-order Bézier curve to smoothen paths with excessively large-angle corners. Simulations conducted on different scenarios and sizes of grid maps indicate that, compared to several classic variants of the A-star algorithm and recent improvements, the improved A-star algorithm not only generates the smoothest and shortest path, but is also more stable in improving time efficiency. By using a new 5-neighborhood and adaptively changing the neighborhood, the improved A-star algorithm addresses the shortcomings of existing improvements that cannot have both search efficiency and path smoothness.