## 4 Avinomics Assessment
### Artificial Intelligence (AI)-based techniques
Optimal performance (i.e., finding the optimal route) is eventually achieved by means of parameter tuning. In comparison with vision-based navigation techniques, such algorithms require no image processing, thus implying no graphics complexities (e.g., resolution, light conditions, etc.). The shared drawback among these algorithms is the slow and/or premature convergence, reflecting reduced navigation accuracy. Representing model-based AI algorithms, training models aim to achieve near-optimal solutions (i.e., best route/path for a UAV) by means of self-training and to learn how to perform continuously better decision-making [117]. On the one hand, Reinforcement Learning (RL) [118; 119; 120] allows UAVs to learn about the environment, yielding optimum navigation. Because an agent and the environment represent the fundamental component of RL, through interaction with the latter, an optimum path can be chosen by the agent (i.e., UAV) using a Markov decision process [121]. From another perspective, based on the reward-to-target compensation system (i.e., the closer the UAV to the target, the more reward given by the environment), a Q-learning algorithm navigates an indoor UAV through the control of a Proportional Integral Derivative (PID) controller [122]. Amid the Q-learning algorithm context, Deep Reinforcement Learning (DRL) can be utilized for UAV navigation by using Q-values, such that the Q-table is replaced with a Deep Neural Network (DNN), hence offering better scalability [123]. Instead of performing repetitive manual calculations, as is the case with the Q-table, the DNN can make predictions and visualization the same way the human brain does [124]. Deep Learning (DL), on the other \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Mathematical-Based AI Algorithms**} \\ \hline
**Algorithm** & **Ref.** & **Performance** & **Efficiency** & **Contribution** \\ \hline PSO & [112] & High & Moderate & Non-feasible paths can be attained by means of an error factor \\ ACO & [113] & Moderate & High & Intra-/inter-colony yield a better convergence toward an optimum \\ GA & [114] & High & High & Chromosome decoding yields path navigation acknowledgment \\ DE & [115] & Moderate & High & Better convergence is achieved by means of selective mutations \\ GWO & [116] & High & High & Flexible algorithm hybridization with UAV navigation-based data \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Assessment of AI mathematical-based algorithms for UAV navigation. hand, with its different types such as Fully connected Neural Networks (FNNs) [125] and Convolutional Neural Networks (CNNs) [126], helps in autonomous UAV navigation under harsh environments by only utilizing the DNN part of the DRL. For instance, a UAV can be navigated through DNN by means of an image augmentation method [127], as well as via real-time photos and CNNs [128]. Despite the numerous advantages offered by training model-based AI in the field of autonomous UAV navigation, such techniques require long training time and large computational power, in addition to presenting high complexity in algorithm implementation, the need for extended information updates, and slow adaptation to new environments [129; 130]. Generally, machine learning algorithms, as in the stated examples of FNNs and CNNs, are restricted by computational power. This is mainly due to their functional mechanism, which resembles heuristic search algorithms. In other terms, such training models use computers to search and resolve the algorithm automatically through massive trial-and-error. The algorithm is hence modeled using parametric optimization, beginning with the framework, equations, and structure defined with initial parameters. The end goal in these training models is to find the optimized parameters, through which the problem (in this case, finding the best route for a UAV) can be solved. Therefore, smarter training models (i.e., with more parameters to achieve a sharper convergence toward an optimal solution) require more computational power.