# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 2 Related Work  
In this section, we introduce related historical research on UAV flight path planning and discuss the remaining issues. We also analyze the problems related to cooperative area search and the energy consumption of multiple UAVs.  
UAV networks have received more and more attention from researchers, and how to plan reasonable flight paths for UAVs has become a hot issue in research in UAV networks in recent years [22]. The path planning problem for a single UAV is considered in [23; 24]. In [25; 23], each UAV considered visits only one target node, so the focus is on collision avoidance between the UAV and obstacles in the environment when performing path planning to ensure that the UAV can complete its task safely. Refs. [26; 27] focused on the collision avoidance problem among individual UAVs considering the existence of multiple UAVs visiting different single target nodes in the same time and space. Refs. [24; 28] investigated the path planning problem of a single UAV visiting multiple target nodes to determine the optimal flight order of the UAV between each target node so that the UAV flies along the shortest path and the energy consumption of the UAV is reduced while performing its mission. When the number of target nodes increases, multiple UAVs can be used to collaborate to access the target nodes together, considering the efficiency of the UAVs to accomplish the task. In refs. [29; 30], the multi-UAV path planning problem of visiting multiple target nodes for a given number of UAVs was investigated and flight paths planned with minimum total UAV energy consumption while completing the target node assignment.  
In the above studies, the study of the energy constraint problem of a single UAV is limited, and the influence of the number of target nodes and the distribution of target node locations on the energy consumption of a single UAV is not deeply explored in the path planning process. However, the energy that a UAV can carry by itself is limited. Therefore, when planning a path for a UAV, the energy consumption limit of a single UAV must be considered, and an energy consumption analysis requires to be performed to ensure that the UAV can visit all the target nodes before running out of energy.  
In ref. [31], an algorithm was used for online cooperative search within an unknown environment by a group of UAVs, which can guarantee maximum coverage in the case of of emergencies but not complete coverage. In fact, none of the existing swarm intelligence methods represents a complete coverage path planning algorithm and none can guarantee complete coverage. Therefore, there is more interest in the academic community in methods based on the idea of task assignments. These methods use certain strategies to divide the task load and extend the coverage path planning approach from a single UAV to multiple UAVs. Table 1 summarizes the above techniques by field of study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**References** & **Single/Multi-UAV** & **Scalability** & **Energy Consumption Constraint** & **Full Coverage** \\ \hline
[23; 25] & Single & Low & No & No \\ \hline
[25] & Single & High & Yes & No \\ \hline
[26] & Multi & Low & No & No \\ \hline
[27] & Multi & Low & Yes & No \\ \hline
[24] & Single & High & No & No \\ \hline
[29] & Multi & Low & Yes & Yes \\ \hline
[30] & Multi & High & No & No \\ \hline
[31] & Multi & High & Yes & No \\ \hline \end{tabular}  
* In the table, four aspects of existing technologies are compared: whether the algorithm supports multi-UAV search, the scalability of the algorithm, whether there is an energy consumption constraint on the UAV, and whether the target area can be fully covered.  
\end{table}
Table 1: Summary analysis and comparison of existing technologies.