Global path planning for mobile robots based on improved A* algorithm
Fanke Kong, Shengju Sang · 2025
To address the issues of excessive path turning points, redundant node expansion, and low operational efficiency in the traditional A algorithm—especially the increased collision risk caused by traversing obstacle-intensive areas—this paper proposes an improved global path planning method. Firstly, a more accurate neighborhood expansion method is adopted on the basis of the $\mathbf{A}^{*}$ algorithm; secondly, a new heuristic function is proposed, which introduces the information of local obstacles in the environment and the distance from the search node to the target point to dynamically adjust the weight of the heuristic function, effectively reduces the number of expansion nodes and prevents the path from crossing dense obstacle regions, so as to improve the operation efficiency of the algorithm; finally, a fold optimization method is used to eliminate redundant inflection points in the path, and a dynamic cut-point method is proposed to smooth the turning points in the path. The experimental results show that the improved $A^{*}$ algorithm can effectively avoid traversing the dense area of obstacles and effectively reduce the algorithm running time, the number of expansion nodes, and the number of path turning points, which indicates the effectiveness and feasibility of the algorithm in robot path planning.