## 2 Related Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Proposed Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{[13]} & A safe path planning algorithm for UAVs that uses a grid-based approach and considers the risk of GNSS signal & The algorithm takes into account the risk of GNSS signal occlusion and is able to plan safe paths in urban environments with high accuracy and efficiency. & The algorithm relies on accurate and up-to-date maps of the environment and may not be able to handle unforeseen obstacles or changes in the environment in real-time. \\ \hline \multirow{6}{*}{[14]} & The paper proposes an Explainable Deep Reinforcement Learning (X-DRL) algorithm for UAV autonomous planning. & The X-DRL algorithm provides a way to interpret the decision-making process of the UAV, allowing for greater transparency and accountability. It also achieves higher accuracy and faster convergence compared to other methods. & The proposed algorithm relies on a pre-defined reward function and may not be suitable for complex environments where the optimal reward function is unknown. \\ \cline{2-3}  & The paper proposes a new UAV navigation method called DNCS, which considers the no-fly zone and efficiently selects the charging station to optimize the UAV’s energy consumption. & The DNCS method can effectively avoid no-fly zones and choose the best charging station based on the UAV’s energy level, resulting in longer flight time and reduced energy consumption. & The proposed method is evaluated through simulations and may not reflect real-world scenarios. The accuracy of the no-fly zone map and the availability of charging stations may also affect the performance of the method. \\ \hline \multirow{6}{*}{[16]} & The paper proposes a highly reliable relative navigation method for multi-UAV formation flight in urban environments using a combination of vision-based and sensor-based techniques. & The proposed method achieves high reliability in relative navigation, even in challenging urban environments with obstacles and GPS-denied conditions. It also allows for flexible formation reconfiguration and can be applied to a variety of multi-UAV formations. & The proposed method relies on the availability of multiple UAVs and may not be suitable for single UAV missions. The accuracy of the method is also affected by the quality of sensor data and the complexity of the environment. \\ \cline{2-3}  & The proposed algorithm can efficiently plan optimal paths for multiple UAVs for energy-efficient data collection in UAV-assisted IoT. & The algorithm relies on accurate and timely communication between the UAVs and the ground station, which can be challenging in certain scenarios. Additionally, the algorithm assumes that the UAVs have unlimited battery capacity, which may not be practical in real-world scenarios. \\ \cline{2-3}  & Deep Q-Network (DQN)-based path planning algorithm for multiple UAVs for energy-efficient data collection in consumption and enabling more efficient data collection. & The proposed algorithm can achieve high positioning accuracy and reliability in GNSS-denied areas leveraging the cooperation of multiple UAVs and the optimization of the belief propagation algorithm. & 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. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._