# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
###### Abstract
This includes adjusting the scale of the map (from coarse to fine) [23] and changing the map representation, e.g., applying the A* algorithm to a hybrid structure of topological and raster maps for more efficient path finding [24]. Such approaches perform well when dealing with maps with large or sparser obstacles but may be much less effective when faced with small scale or highly dense obstacle environments. The second direction is to combine the A* algorithm with a series of local path planning algorithms to enhance its obstacle avoidance capability in practical applications. For example, integrating the A* algorithm with an artificial potential field method [25] or integrating the A* algorithm with an improved dynamic window approach (DWA) [26; 27; 28] are attempts that aim to enable AGVs to avoid obstacles in real time while path planning. However, while this combination improves obstacle avoidance performance, it also significantly increases the overall execution time of the algorithm. Aiming at the common path redundancy and zigzag problem in A* algorithmic path planning, academics have also explored various path post-processing techniques, such as the dynamic tangent method [29], minimisation of turns [30], and Bessel curves [31], which aim to optimise the smoothness of paths by adjusting them after the fact. However, it is worth noting that although these post-processing strategies can improve the path morphology, since they are adjusted after the path planning is completed, the final generated paths may not be fully compatible with the actual motion characteristics or trajectory requirements of the auto-navigated vehicles. Therefore, the main contributions of this paper can be summarised as follows:  
1. This paper innovatively incorporates a dynamic weighting mechanism based on the local obstacle rate into the heuristic function, and it cleverly combines the considerations of directional guidance and turning cost. This design aims to enable the path search process to flexibly respond to the environmental characteristics of the current node and dynamically adjust the surrogate value of the search path, so as to not only optimise the path length, but also significantly reduce unnecessary turns and enhance the smoothness and efficiency of the path. 2. For the different effects of local obstacle rates, this paper proposes an innovative double-layer neighbourhood search method. The method intelligently selects different ranges of search neighbourhoods based on the actual values of local obstacle rates. This strategy effectively addresses the challenge of path planning under different obstacle density environments and aims to achieve more flexible and efficient path optimisation to ensure that the optimal path can be found in various complex scenarios. 3. In order to further improve the performance of the algorithm, this paper also introduces the simulated annealing algorithm, which effectively reduces the complexity of the path node selection and the number of comparisons, accelerating the path search process. In addition, this paper also adopts [PERSON]'s algorithm for the post-processing of the initially generated paths, which further refines the paths by deleting redundant nodes, ensuring the simplicity and practicality of the paths. The architectural layout of the article is as follows. In Section 2, we deeply analyse the core principle of the A* algorithm, and we meticulously discuss the limitations and problems that the algorithm may encounter in practical applications. In Section 3, we elaborate on a series of improvement measures for the shortcomings of the A* algorithm, especially focusing on the application of dynamic neighbourhood and the optimisation adjustment of the heuristic function based on the local obstacle rate, with a view to enhancing the performance and adaptability of the algorithm. Immediately after that, in Section 4, in order to verify the effectiveness of these improvement strategies, we carefully design three sets of simulation experiments.