# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
###### Abstract
As far as the neighbourhood search is concerned, although the search process can be optimised by expanding [11] or reducing the search neighbourhood [12], the traditional fixed neighbourhood size approach lacks flexibility and cannot be dynamically adapted to specific situations. In the field of improvement of heuristic functions, a common strategy is to adjust the computational model of distance, such as introducing weighted distance [13], which incorporates a variety of distance formulas. But it is important to note that the combined distance formulas may not more accurately reflect the actual path costs. However, many researchers are committed to incorporating more diversified heuristic elements into the heuristic function, such as terrain features [14], which enhance the nuance of path evaluation by assigning specific risk cost and accessibility values to different terrains. Other scholars have considered factors such as the UAV's (unmanned aerial vehicle's position, fuel consumption, and navigation time [15], as well as weather threats [16], and normalised these variables to enrich the evaluation dimensions of the heuristic function. However, with the increase in factors in the heuristic function, although it can reflect the environmental information and variable changes more comprehensively, it also brings about the increase in uncertainty in the output value of the evaluation function, as well as the increase in complexity in the selection of the optimal solution under multi-factor decision making. This is similar to a multi-objective optimisation problem, which makes the algorithm face greater challenges in finding the global optimal solution. To overcome this challenge, some scholars have explored the use of neural networks to train heuristic functions [17] with a view to predicting path costs more accurately by means of machine learning. However, this method requires a high scale and quality of the dataset, and the training process is time-consuming, so there are some limitations in practical applications. In terms of node expansion strategy, some scholars have also put forward innovative ideas, such as by preprocessing the map, using cluster analysis and other techniques to identify regions with dense obstacles, and giving priority to sampling and node expansion in these regions [18]. This approach helps to reduce unnecessary search space and improve search efficiency. Some scholars have also introduced the \"suboptimal\" strategy of node comparison and updating [19]. Although this method shows some potential in node optimisation, it still relies on a large number of comparison and screening processes, and the computational cost cannot be ignored. In addition, there are also researchers working on the development of variants of the A* algorithm and its further optimisation, such as incorporating directional constraints into the two-way search framework to optimise the expansion of the optimal nodes [20]; adapting the core expansion mechanism of the \"matrix expansion A* algorithm\" to explore new search paths [21]; and developing a \"critical-point planning path\" in the \"Critical Point Planning Path\" approach focusing on the selection of \"suboptimal nodes\" by updating the search strategy, aiming to find more flexible or adapted paths to specific conditions [22]. These variants and improvements of A* algorithms have their own characteristics but face the same challenge that each method has its own optimal application scenarios, making it difficult to generalise to all types of map environments. Therefore, the selection of the appropriate algorithm or variant for a practical application needs to be weighed against the characteristics and needs of the specific problem. The integration strategies of these algorithms can also be summarised in two main directions. The first direction is the integration with other \"graph search techniques\", which focuses on innovations in map processing strategies.