# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 1 Introduction  
Unmanned aerial vehicles (UAVs) are autonomous aerial devices that do not need to be manned by humans [1]; this condition has made them very popular and helpful for various applications in different areas, such as agriculture, photometry, rescue, the army, communications systems, etc. Deploying individual or group UAVs (i.e., forming networks) allows exploration in very complex, dangerous, and difficult-to-reach places, enabling significant savings in time and cost and decreasing the loss of human lives [2]. Furthermore, UAVs can be stand-alone structures or complement communication systems such as cellular networks [3]. Communication systems using UAVs (UAV-CS) have become very prominent due to their low cost and rapid network deployment, which are used in post-disaster scenarios and help save human and material lives [2]. UAVs, part of a communications system, can be used in various areas, such as base stations (AUV-BS), access points (UAV-AP), routers, and gateways. UAVs can be controlled by human assistance, but they can also be autonomous, which is a significant challenge in 3D path planning. Path planning aims to enable UAVs to move (fly) in a path free of obstacles to avoid collisions considering route integrity (coverage of the entire desired route) and optimality (e.g., the shortest path or a path that produces less energy consumption). Table 1 summarizes the studies related to 3D path planning, demonstrating the uniqueness of our research. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Ref** & **Year** & **Description** & \begin{tabular}{c} **Challenges** \\ **Information** \\ \end{tabular} & **3D** & \begin{tabular}{c} **Objective to Be** \\ **Optimized** \\ \end{tabular} & **\
\begin{tabular}{c} **Multiple** \\ **UAVs** \\ \end{tabular} & **Obstacles** & **Environment** & **Opt. Variable** &
\begin{tabular}{c} **Heuristic** \\ **Sol.** \\ \end{tabular} & **A.I. Sol.** \\ \hline
[4] & 2023 &
\begin{tabular}{c} Review of vision-based \\ navigation techniques for \\ Unmanned aerial Vehicles \\ \end{tabular} & ✓ & ✓ & ✓ & X & X & X & X & ✓ & ✓ \\ \hline
[5] & 2019 &
\begin{tabular}{c} Survey on coverage path \\ planning with unmanned aerial vehicles \\ \end{tabular} & X & ✓ & ✓ & X & ✓ & X & X & ✓ & X \\ \hline
[6] & 2023 &
\begin{tabular}{c} Review on viewpoints and \\ path-planning for UAV-based \\ 3D reconstruction \\ \end{tabular} & ✓ & ✓ & X & X & X & X & X & ✓ & X \\ \hline
[7] & 2021 &
\begin{tabular}{c} Model-based analysis of \\ multi-UAV path planning for \\ surveying roadissater \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & X & X & X & ✓ & X \\ \hline
[8] & 2023 &
\begin{tabular}{c} Surveys on bio-Inspired \\ Optimization-Based Path \\ planning Algorithms in UAVs \\ \end{tabular} & ✓ & ✓ & ✓ & X & ✓ & ✓ & X & ✓ & X \\ \hline
[9] & 2019 &
\begin{tabular}{c} Survey of Three Dimensional \\ Flight Path Planning for \\ Unmanned Aerial Vehicle \\ \end{tabular} & X & ✓ & X & X & X & X & X & ✓ & X \\ \hline
[10] & 2018 &
\begin{tabular}{c} Survey on computational \\ intelligence-based UAV \\ path planning \\ \end{tabular} & ✓ & ✓ & X & X & X & X & X & X & ✓ \\ \hline
[11] & 2022 & \
\begin{tabular}{c} Survey on UAV path \\ planning using an \\ optimization approach \\ \end{tabular} & ✓ & ✓ & ✓ & X & ✓ & X & ✓ & X & ✓ & ✓ \\ \hline Our & 2023 &
\begin{tabular}{c} Studies 3D path planning \\ algorithms in UAV-enable \\ Communications Systems \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & x \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of path planning surveys. Prior research has investigated UAV planning in 2D. For instance, in [12], the authors discuss the path-planning problem involving continuous interaction with the environment and provide an overview of motion planning algorithms. In [13] the authors examine state-of-the-art path planning in automation, robotics, UAVs, and video games, considering as a secondary feature 3D path planning.