## 1 Introduction
The fusion algorithm not only guarantees a smoother and shorter global optimal path but also effectively avoids dynamic obstacles [27]. An improved A* algorithm combined with the dynamic window method, which reduces the number of inflection points by selecting key points, was proposed [28]. An improved A* algorithm that takes the travel time of the mobile robot as the cost and can adjust the weight of the heuristic function according to obstacle information is proposed, effectively reducing the number of turns and the turning angle during path planning [29]. An improved real-time path planning algorithm combining the A* algorithm and the dynamic window method is proposed. The search point selection strategy and the evaluation function are optimized to improve the search efficiency of the A* algorithm [30]. In the evaluation function of the traditional A* algorithm, the obstacle information in the environment and the cost information from the parent node to the target point are added to improve the efficiency of path search, optimizing the node expansion direction and effectively improving the smoothness of the path [31]. A topology map with larger granularity is built on the traditional grid map and the paths planned by the topology layer are then optimized on the grid map to remove redundant nodes and improve the smoothness of the paths [32]. By smoothing the global planning path, the stability of a USV heading in the navigation is Improved, and by adding the key points of the global path, a guide path is formed to unify the terminal heading range [33]. The extraction of key turning points combined with the Dynamic Window Approach significantly enhances the smoothness of the path [34]. An enhanced approach, which combines the A* and DWA algorithms, is introduced. This approach establishes a connection between the target point and the current point while omitting the consideration of the three rearward search directions. This reduction effectively reduces the computational load during the search process [35]. To address the aforementioned challenges comprehensively, this study presents a novel fusion algorithm that combines the enhanced A* algorithm and the DWA algorithm. Thisinnovative approach focuses on optimizing various aspects of the A* algorithm, including the evaluation function, search methodology, key node extraction, and integration of the DWA algorithm. The experimental outcomes substantiate the effectiveness of this approach. The path planning method introduced in this study empowers AGVs with real-time obstacle avoidance capabilities while simultaneously ensuring the maintenance of a globally optimal path. The results underscore the potential of the fusion algorithm to overcome the limitations of individual algorithms and deliver a more robust and efficient path-planning solution for AGVs. The main contributions of this study are summarized as follows:  
* The path planning algorithm proposed in this study, which combines improved A* and DWA, can be widely applied across various domains, including automated unmanned warehouses, logistics and warehouse management, healthcare and medical facilities, ports and logistics centers, the hospitality and service industries, as well as autonomous driving. This algorithm has the potential to actively drive the rapid development of industrial automation. * By incorporating an obstacle weight coefficient into the estimated cost component of the evaluation function, the algorithm adapts to changes in the number of obstacles in the environment, thereby enhancing search efficiency. Selecting different search approaches based on the obstacle positions ensures both path safety and improved search speed. The extraction of key nodes results in a final global static path that is both secure and optimal. * Adjusting the orientation angle based on information from the first key node optimizes the path length while incorporating local target points effectively mitigates the issue of the DWA algorithm falling into local optima.