## 2 Related Review  
The purpose of this review is to provide a comprehensive overview of recent research on Navigation Systems-based UAV Path Planning methods and Optimization Techniques-based UAV Path Planning methods. As emphasized in the introduction section, these methods are crucial in the field of Unmanned Aerial Vehicles (UAVs). In this review, we present two tables that showcase the latest research on these path planning methods, highlighting their significance and relevance in the UAV industry. By examining these examples, readers will gain a deeper understanding of the current state of research in this field and the advancements made in recent years. Tables 1 and 2 outline the advantages and limitations of each method. Navigation Systems-based UAV Path Planning methods utilize GPS and sensor-based technologies to plan and optimize paths for UAVs, while Optimization Techniques-based UAV Path Planning methods rely on mathematical algorithms and models to find the most efficient path for UAVs. Navigation Systems-based UAV Path Planning methods offer several benefits, including real-time capabilities that enable adjustments to the UAV's path while in-flight. Additionally, these methods are relatively easy to implement and applicable to a wide range of UAV applications. However, their reliance on sensors and GPS could limit their effectiveness in areas with poor signal coverage. On the other hand, Optimization Techniques-based UAV Path Planning methods can provide optimized paths for UAVs in complex environments. These methods can consider various constraints and objectives, making them suitable for a wide range of applications. However, they may require significant computational resources, making them less suitable for real-time applications. To sum up, Navigation Systems-based UAV Path Planning and Optimization Techniques-based UAV Path Planning methods each have their own strengths and weaknesses, and the selection of the most suitable method for a particular UAV application will depend on specific requirements. This review has shed light on the capabilities and limitations of each approach, which will be helpful for researchers and practitioners in making an informed decision about the path planning method that best meets their needs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Proposed Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{[8]} & A deep reinforcement learning & \multirow{2}{*}{ \begin{tabular}{c} Can handle high-dynamic \\ environments, is adaptable to \\ different environments, can learn \\ from experience, and requires \\ minimal human intervention. \\ \end{tabular}  } & Needs a large amount of data for training, may suffer from safety \\  & & & \\ \cline{1-1}  & &  
* [18] Optimized belief propagation-based cooperative navigation algorithm for multiple UAVs in GNSS-denied areas using only onboard sensors. The proposed algorithm relies on the availability of multiple UAVs and assumes that they can communicate with each other in real-time, which may not always be feasible in practical scenarios. Additionally, the algorithm may require significant computational resources, which may limit its real-time applicability.