# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 3 Methodology: Mapping Study
### Planning
#### 3.1.4 Classification Scheme and Data Extraction  
A classification scheme organizes critical information extracted from selected papers, enabling us to answer previously raised RQs. In this work, a part of the scheme is elaborated from existing classifications in Sections 2.4 and 2.5. Subsequently, this scheme is completed and refined, including new categories (for example, new path planning techniques) or dividing types into subcategories (for example, classification and subclassification of path planning techniques). In addition, for the classification of information, ref. [1] is taken as a reference to establish well-defined categories. Figure 9 shows the dimensions and attributes coded at this stage. Figure 9 shows the information to be extracted during the coding stage of the selected papers. Four main dimensions (yellow boxes) have been created: the RQs posed in the Planning stage. In turn, each dimension has a certain number of attributes (light blue boxes), which are those data that need to be extracted from each of the contributions. From the same paper, one or several attributes can be extracted for a certain dimension. Each of these dimensions and their attributes is described below. * **Target to be optimized (minimized or maximized):** This dimension identifies the target for optimal path planning of UAVs in a 3D environment, with the following categories considered:  
Figure 9: Classification scheme. 1. **Cost:** Refers to the cost associated with path planning, which may be the cost of batteries, hardware and software fees, additional charges such as cameras used to capture the images, sensors to detect the environment, GPS to locate the position, and cost of initial manufacturing and maintenance. 2. **Time:** It is generally associated with minimizing the UAV path's time or the UAV communication's information transmission time. 3. **Energy:** Refers to the minimization of energy consumption to complete its mission. 4. **Multiple:** This category considers more than one objective (objective function) to optimize. 5. **Others:** This field includes additional attributes to those mentioned above. **System Formulation:** This dimension defines the specific problem addressed in UAV path planning in 3D. It is composed of the number of UAVs, obstacles, and environment. 1. **Number of UAVs:** It means the number of UAVs used in the formulation of the problem, could be: 1. **Simple:** It means that only one UAV was used. 2. **Multiple:** It means that more than one UAV was used. 2. **Obstacle:** Anything that interrupts the flight of the UAV is considered an obstacle. The types of obstacles considered are technical and non-technical. 1. **Technical:** Constraints that are developed to successfully and economically complete a task. 2. **Non-technical:** They can be static or dynamic limitations. Static obstacles are made up of buildings, mountains, trees, etc. Dynamic obstacles are unexpected threats, such as a bird. 3. **Environment:** In UAV path planning, it is essential to know the environment in which the UAV has to fly. The environment can be static or dynamic. 1. **Static:** It means that the environment will not change throughout the route. 2. **Dynamic:** It means that the environment is uncertain; it will constantly be changing. **Optimization variables:** These are the model variables that can be optimized. The variables considered for optimization are altitude and distance. 1. **Altitude:** It is the height of the UAVs with respect to the ground. 2. **Distance:** It is the distance that the UAVs must travel to complete the mission. 3. **Multiple:** It means that there may be more than one variable to optimize. 4. **Others:** It means that there may be other variables to optimize. **Solution Strategy:** Covers all aspects used to solve the problem. As strategies, exact and heuristic algorithms have been considered. 1.