# UAV Path Planning Model Based on R5 DOS Model Improved A-Star Algorithm
## 1 Introduction  
With the progress of science and technology, the development and application of various types of robots and remote sensing [1] have become more advanced and they are increasingly entering all aspects of modern life. Within this trend, UAVs are now widely used in agriculture [2, 3], forestry, smart healthcare [4, 5], geological exploration [6], disaster relief [7] and for military purposes. [PERSON] et al. conducted a comprehensive investigation and discussion on green technology and application, current challenges and the future direction of UAV-Fog [8]. [PERSON] et al. designed a multi-UAV and multi-IoT network, saving energy and time of the model [9]. [PERSON] et al. optimized the position, unloading decision and resource allocation of UAVs [10]. [PERSON] et al. proposed a layered architecture to improve the capacity and coverage of UAV-mounted BS [11]. In Chinese agriculture and forestry, UAVs [12, 13] are extremely important; however, they face many obstacles during operation. In agricultural work, common obstacles are trees, utility poles, flocks of birds and houses. In forestry work, obstacles are mainly trees and flocks of birds. Thus, the core problem of UAVs is how to effectively avoid obstacles during operation. In response to this problem, many researchers have developed a number of path planning and obstacle avoidance algorithms [14, 15, 16]. Examples of these algorithms include: the Dijkstra algorithm, the ant colony algorithm, the A-star algorithm and the artificial potential field method. Among them, the artificial potential field method has the advantages of a simple principle and a smooth pathway, but it also has disadvantages such as easily falling into local optimization and being unable to avoid large obstacles. To optimize these problems, scholars locally and internationally have conducted significant research. [PERSON] et al. proposed a three-dimensional artificial potential field method based on the pilot method, which solves the problem of the UAV falling into a limit value in theprocess of obstacle avoidance [17]. [PERSON] et al. proposed an improved artificial potential field method using the regular hexagon guidance process to improve the local minimum value problem [18]. [PERSON] et al. combined and improved the artificial potential field method and the A-star algorithm, so that the path smoothing of the UAV during obstacle avoidance is more in line with the actual situation of the UAV [19]. [PERSON] added a regulator between the repulsive functions, which further improved the artificial potential field method [20]. Additionally, [PERSON] et al., through combining virtual structures with the artificial potential field method, reduced the amount of computation and increased the flexibility of unmanned ships [21]. Many of the above researchers have made a lot of improvements to the artificial potential field method. However, when operating unmanned aerial vehicles, it is possible that large obstacles will be encountered where the artificial potential field method struggles to work effectively. This can be significantly improved by using the traditional A-star algorithm in the three-dimensional space. To solve this problem, this paper proposes an artificial potential field method based on the R5 DOS model and an improved A-star combined algorithm in three-dimensional space. The model combines an artificial potential field method and an A-star algorithm in three-dimensional space, which improves the search efficiency of the A-star algorithm and avoids the problem of the artificial potential field method falling into the local minimum value. Our work scope is mainly around the improvement of A-star in three-dimensional space, and the effectiveness and progressiveness of the improved A-star algorithm are studied through simulation experiments. The main contributions and innovations of this paper are as follows:  
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