# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 2 Background
### 3D Path Planning  
From the beginning, UAV path planning investigations were carried out only considering the position in two dimensions, without considering the height that the UAV must have. Over time, the researchers estimated the optimal position in the 3D dimension, taking factors such as a canal model for the rural, urban, and dense urban environments. This factor increases the complexity of the problem and is closer to the reality that UAVs must face in a communications system [1; 3]. Three-dimensional path planning is a very complex topic. It has been investigated in recent years since it has the difficult task of finding the best collision-free route, defined as the path planning of the UAV from an initial position to an objective position (destination). The path is planned to minimize the completion time for a particular mission [15]. Figure 2: 3D communication network (high-altitude long-range communication and low-altitude high-density communication) [3]. UAV path planning can be classified into two groups. The first group refers to path planning from start to finish, where the UAV must move between two fixed points while avoiding threats and obstacles. Meanwhile, the second group is coverage path planning, where the UAV will plan a path that covers the entire area as it stays away from obstacles [1]. This approach has changed, and there has been a greater interest in 3D perception for path planning, where perception constraints are considered in the planning problem. The methods developed in this field consider the quality of perception to reduce uncertainty in state estimation. That can be achieved by ensuring that particular objects and features remain within the limited detection field of view of the UAV [2]. In addition to traditional path planning methods, alternative approaches utilize constraint methods in 3D path planning. Constraint methods aim to achieve an optimal global path while maintaining a safe distance from obstacles in free space. However, these methods often exhibit conservative dynamic constraints, resulting in slower flight speeds. Another approach employs smooth constraints that leverage gradient information to maneuver obstacles away from the path. While this approach mitigates the risk of local minima, ensuring kinematic feasibility becomes a challenge [19]. Conversely, in the optimization of UAV paths, it is imperative to minimize factors such as cost, energy, and time. Path planning must strike a balance in minimizing these three elements. Efficiently optimizing the mission time of the UAV entails reducing battery consumption for completing the task, thereby decreasing path planning costs. Likewise, power optimization necessitates accomplishing the mission within the shortest feasible timeframe, leading to reduced path planning expenses [1]. Analyzing and considering these factors are essential for optimizing UAV path planning. Based on these considerations, the following points are taken into account:  
* **Cost:** UAV path planning is considered profitable when the total cost of path planning decreases, such as the cost of batteries, hardware and software fees, additional charges such as cameras used to capture the images, sensors for detecting the environment, GPS to locate the position, initial cost of manufacturing and maintenance, etc. [1]. * **Efficiency in Time:** UAV path planning is considered efficient when the path is completed in the shortest possible time. This is achieved by optimizing a path and making it collision free from source to destination. An example of optimizing the communication time for fixed-wing UAVs is achieved by strategically planning the paths of the UAVs to minimize information transmission duration [15]. * **Energy Efficiency:** Energy efficient path planning is considered when UAVs use the minimum energy to complete their mission. The UAVs' energy is optimized to achieve the mission and return to the destination [1].