## 1 Introduction  
With the continuous development and progress in science and technology, the increasing number of private cars makes traffic network congestion a topic of both theoretical interest and practical importance [1]. The Automated Guided Vehicle (AGV), serving as the primary tool for intelligent material transportation, has gained widespread utilization in modern production systems. The incorporation of AGVs offers several advantages, including enhanced productivity, reduced labor costs, improved energy utilization, and heightened safety [2]. Path planning stands as a pivotal aspect of AGVs, with key metrics for assessing path quality encompassing path length, smoothness, and safety. Currently, numerous AGV path-planning algorithms cater to diverse environments. Common static global path planning algorithms encompass the A* algorithm [3, 4], [PERSON]'s algorithm [5, 6], ant colony algorithm [7, 8], genetic algorithm [9], and particle swarm optimization algorithm [10, 11]. Meanwhile, local path planning algorithms comprise the dynamic window approach (DWA) [12, 13], the artificial potential field method [14, 15], and the time elastic band (TEB) algorithm [16]. Among the global path planning algorithms, the A* algorithm stands out as the most prominent and widely employed. However, several issues arise in the paths generated by the A* algorithm, including an excess of redundant nodes, unnecessary turning points, simplification leading to a point without considering AGV size, and an inability to navigate temporary obstacles. Concerning local path planningalgorithms, the DWA algorithm exhibits commendable local obstacle avoidance capabilities, yet it is prone to becoming trapped in local optima. To solve the above problems, an improved A* algorithm for 8-neighborhood node search, which effectively improved work efficiency and security, was put forward [17]. An improved A* algorithm combined with the hop search algorithm, which reduced the amount of calculation, was proposed [18]. A time factor, which effectively reduced the number of turns, was introduced [19], and an improved A* algorithm for optimal path planning of robot energy consumption that takes the energy consumption value as the traffic cost between nodes was also put forward, thus obtaining an optimal path of global energy consumption [20]. By introducing the neighborhood matrix, an effect comparable to obstacle expansion is achieved during mapping, consequently enhancing path safety. Moreover, the heuristic function received an upgrade through the amalgamation of angle and distance information, effectively enhancing calculation efficiency [21]. A novel DWA algorithm was introduced to detect dense objects, allowing AGVs to adeptly navigate around congested regions [22]. An adaptive version of the DWA algorithm aimed at ensuring path safety and smoothness was also proposed [23]. To further enhance control efficacy beyond conventional methods, two fresh evaluation functions were incorporated [24]. An improved DWA approach that takes into account the spatial relationship between the mobile robot's size and obstacles was put forth, thus contributing to better path planning [25]. However, it is worth noting that this version is susceptible to local optimization. However, the above-mentioned improved A* algorithm only considers the globally static path and cannot realize real-time dynamic obstacle avoidance. The improved DWA algorithm can realize real-time dynamic obstacle avoidance but it cannot achieve the globally optimal path; both cannot be satisfied at the same time. Therefore, an improved fast and smooth A* algorithm designed by dynamically changing the search step length and adding a path deviation component to the evaluation function of the DWA algorithm is proposed [26]. By converting a three-dimensional city map into a two-dimensional plane map, the Floyd algorithm is used to smooth the global path so that the calculation speed is improved.