In response to the challenges associated with suboptimal route efficiency, insufficient environmental adaptability as well as unsmooth paths in global path planning for mobile robots using the conventional A* algorithm, this paper introduces an adaptive A* algorithm. Initially, an adaptive estimation function is put forward by utilizing the positional relationship between the robot’s current and target position. Through tuning the coefficients with the heuristic function dynamically, path generation time is curtailed. Subsequently, the distance function model is optimized. The arithmetic mean of Euclidean distance and Manhattan distance is utilized to enhance the algorithm’s adaptability to diverse environmental maps. Ultimately, the redundant point deletion strategy is implemented to remove unnecessary nodes along the route, thereby enhancing path smoothness. Experimental results show that across three varying maps, the proposed algorithm, relative to the conventional A* algorithm, on average achieves a 69% reduction in path generation time, a decrease in path length of 2.66 m, and a decline in the quantity of mean steering angles exceeding or equaling 45 degrees of 38.1%. Moreover, when compared with several classic A* algorithm variants and recent improved algorithms, the proposed approach is capable of generating the shortest and most smooth path, confirming its superior planning performance while fulfilling both efficiency and smoothness demands.