## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Based Routing Protocol
#### 6.2.2 Self-Adaptive Learning-Based Routing Protocols
For the ground component, the vehicle maintains a fixed-size Q-table that converges to an optimally designed reward function. It forwards the routing request to the optimal node by looking up the filtered Q-table along the global routing path. The simulation results show that the proposed QAGR method performs well in packet delivery and end-to-end delay. QL-based adaptive swarm control inspired routing protocol in UAV networks (QRIFC): in [154], the authors proposed the QRIFC scheme as a location-based multi-hop routing protocol integrating the QL algorithm. The authors also proposed the AFCA protocol to construct the structure of a UAV swarm and then to determine the 3D LD. It is then used by QRIFC to make routing decisions. AFCA controls the relative mobility with neighboring UAVs and provides a relatively stable state for QRIFC. A novel multi-objective reward function for QL is designed to minimize latency and energy consumption. QRIFC optimizes mobility, coverage, and QoS in the connection by imposing minimum and maximum distance constraints between UAVs. It uses two-hop neighbor information and provides stable link time (LD). This strategy expands the local view of each UAV to select a more stable path. The proposed QRIFC outperforms existing routing protocols by 21-40% in average end-to-end delay and 9-23% in average packet delivery ratio, in addition to fewer retransmissions. Table 8 summarizes the advantages and limitations of some prominent routing algorithms in AI-based UAV networks, including their classification and a description of their roles in specific applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Ref.** & **Algorithm** & **Main Contribution** & **Advantage** & **Limit** \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Based on network structure \\ \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{c} [147] \\ \end{tabular} } & \multirow{8}{*}{PARIoT} & Deployment in different application models of UAV networks & Ability to predict UAV movements to optimize routing, increase robustness, and reduce latency in ad-hoc networks & Depends on the accuracy of motion prediction; performance degrades in rapidly changing environments \\ \cline{2-5}  & & & UAV network for environmental monitoring and emergency & Improve connectivity performance and routing time between collaborating UAVs & Performance depends on the accuracy of the Gaussian distribution model; limited scalability in large networks \\ \cline{2-5}  & & & & Optimal route selection is based on incorporating the predicted locations of intermediate nodes in a transmission session into the path selection criteria & High computational cost, especially in dynamic networks; not suitable for large networks \\ \cline{2-5}  & & & & QGeo reduces network costs and increases packet delivery rates in mobile robot networks using reinforcement learning & Depends on Q-learning parameter settings; performance degrades in highly dynamic and complex network structures. \\ \cline{2-5}  & & & & QGeo reduces network costs and increases packet delivery rates in mobile robot networks using reinforcement learning & Depends on Q-learning parameter settings; performance degrades in highly dynamic and complex network structures. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Some prominent AI-based routing algorithms.