# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 1 Introduction  
In the context of today's rapid technological development, Unmanned Aerial Vehicles (UAVs) are characterized by low cost, high speed, and high mobility [1, 2], and have gained popularity due to their large number of applications in aerial photography [3, 4], 3D mapping [5], search and rescue operations [6, 7, 8], precision agriculture [9], and cargo transportation and delivery [10]. In these tasks, UAVs must fly along the designed path, and the path planning problem becomes a key issue to ensure obstacle avoidance and efficiency in accomplishing a specific task [11, 12]. The path planning problems for UAV flight can be categorized into two types: the goal-oriented problems [13] and the coverage path planning (CPP) problems [14, 15]. The goal-oriented path planning methods aim to find the safest, most efficient, collision-free, and least-cost travel paths from one point to another. The optimal path will be decided based on constraints such as the shortest path between endpoints, i.e., the minimum time to travel without any collisions. The objective of the CPP problem is to cover the total area of interest with minimum overlapping, and the goal of the CPP algorithms is to minimize the total coverage path and execution time. There has been much research in the literature on the CPP problems of UAVs. [PERSON] et al. [16] introduced a coverage path planning formation approach with a leader-follower formation approach and presented a FM\({}^{2}\)-based collision avoidance method for multi-UAVteams in urban environments. A path planning method based on the boustrophedon flight pattern was developed to cover the target area and computationally generate smooth paths to ensure that the UAVs could pass through predefined observation points. [PERSON] et al. [15] proposed a vehicle-UAV collaboration-based efficient CPP method for large-area segmentation. A coverage area segmentation method based on dynamic adjustment was studied, and the cooperative coverage paths were generated based on the ant colony optimization algorithm and zigzag path planning method. [PERSON] et al. [17] developed an efficient planner for computing the optimal edge-vertex back-and-forth path, taking into account the starting and ending points. [PERSON] et al. [18] presented a new traversal collision-free trajectory generation algorithm based on clothoids, which is suitable for covering a continuous two-dimensional (2D) environment. However, most of these CPP methods focus on 2D planar path planning and ignore the mountain features in the terrains, resulting in poor coverage effects and low sweeping efficiency. Shortest path planning is another key factor to reduce energy consumption during UAV flight. The common shortest path planning methods for UAVs include the rapidly-exploring random trees (RRT) algorithm [19], the Randomized Path Planner (RPM) approach [20], the A* algorithm [21], the genetic algorithm [22; 23], particle swarm algorithm [24], and ant colony algorithm [25]. It is noted that these path planning methods are mainly dedicated to find the safest, collision-free, and least-cost travel paths from one point to another. The generated optimal path will be decided based on constraints such as the shortest path between endpoints, i.e., the minimum time to travel without any collisions. Although these methods pose the ability to generate the shortest path between endpoints for reducing energy consumption of UAVs, they are not applicable to the region-covering scenarios. However, the concept of the shortest path can be used as an important evaluation indicator for the CPP problem. Thus, by minimizing the total length of the UAV coverage path, the energy consumption of the drone can be reduced, and the coverage efficiency can be improved.