# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 4 Results
### RQ3--Are There Approximate Methods for Path Planning in 3D UAVs? RQ3 seeks to provide information on the approximate methods mainly addressed for path planning in 3D UAVs. To answer this question, the results obtained in this research are presented below. Table 10 presents the classification of the algorithms, in which 29 out of the 37 contributions analyzed, the researchers use the method of bio-inspired algorithms, such as the PSO. However, the researchers note that using this algorithm for a global optimal solution can take a long time (A-14). The 18.9% (A-17, A-19, A-24, A-27, A-30, A-31, and A-34) of the papers analyzed use methods based on sampling. Multi-target exploration and path planning for unknown and unstructured environments are performed using the RRT algorithm, specifically designed for use in 3D exploration missions (A-19). The node-based method is used by 21.62% of the contributions (A-01, A-04, A-16, A-17, A-22, A-34, A-35, and A-36). One of the algorithms implemented is the Layered Essential Visibility Graphic (LEVG) method for smooth path planning for fixed-wing UAVs in 3D environments through an efficient branching algorithm to reduce the computational time (A-35). Table 11 shows the algorithms used in the 37 contributions analyzed. Likewise, Table 12 summarizes the information extracted from the contributions in this systematic mapping study. The researchers have employed diverse heuristic algorithms to address UAV-enabled communications systems' 3D path planning problem. The algorithms are \begin{table}
\begin{tabular}{l c} \hline \hline
**Algorithm Classes** & **Total** \\ \hline Based on Sampling & 7 \\ \hline Based on Nodes & 8 \\ \hline Bio-inspired & 29 \\ \hline Other & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Types of heuristic algorithms used in path planning. Figure 14: Variables to optimize in the analyzed articles. The optimized multiple variables are observed in lead color, and the predominant variables in blue and orange. classified into four categories based on their characteristics: Sampling-based, Node-based, Bioinspired, and Others. Among the algorithms, A* and [PERSON]'s algorithms are commonly used in path planning and are categorized as node-based. They are well-known and widely adopted due to their effectiveness in finding optimal paths. The Heuristic algorithms section includes various optimization techniques such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Gray Wolf Optimizer (GWO). These bioinspired algorithms have been adapted and utilized in 3D path planning to optimize objectives like energy consumption, cost, time, and collisions. Additionally, some algorithms incorporate specific techniques to address challenges in UAV communications systems. For instance, the Energy Efficiency A* (EEA*) algorithm focuses on energy optimization, while the Convex Model Predictive Control (MPC) algorithm considers dynamic obstacles. The algorithms demonstrate the efforts of researchers to adapt and develop innovative solutions for UAV 3D path planning, catering to various optimization objectives and environmental conditions. While some algorithms are based on well-established optimization techniques, others introduce novel adaptations and combinations to tackle specific challenges.