## 4 Avinomics Assessment
### Artificial Intelligence (AI)-based techniques  
Unlike vision-based navigation techniques, Artificial Intelligence (AI) contributes toward better autonomy in UAV navigation [101], mainly by allowing drones to learn from their surroundings [102]. From another perspective, AI allows for automatic feature extraction while better managing onboard resources, which differentiates it from traditional cognitive algorithms [103]. Concerning UAV navigation, AI algorithms are characterized in this manuscript according to their paradigm as follows: AI algorithms that involve mathematical model formulation to find the best solution to a given problem by relying on predefined rules and objectives to guide the UAV are grouped in the first set (i.e., mathematical optimization) [104]. The other set emphasizes a paradigm that trains the models to make better decisions in UAV navigation (i.e., performance is evolved over time based on training data and gained experience) [105]. 1. Mathematical optimization  
For any given non-deterministic polynomial complex problem (i.e., the navigation path of a UAV), mathematical optimization-based AI approaches achieve near-optimal solutions [106]. The most dominant algorithms in this field are Particle Swarm Optimization (PSO) [107], Ant Colony Optimization (ACO) [108], Genetic Algorithm (GA) [109], Differential Evolution (DE) [110], and Gray Wolf Optimization (GWO) [111], as assessed in Table 2 and defined below: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Vision-Based Navigation for UAVs** & \\ \hline
**Advantages** & **Disadvantages** & **Challenges** & **Field of Application** \\ \hline \multirow{3}{*}{\(\checkmark\) Informative scene data} & \(\mathsf{X}\) Complex environment & \multirow{3}{*}{Real-time processing requirements} & \multirow{3}{*}{Agriculture} \\  & structures reflect complexities in the navigation algorithm & & \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & Anti-jamming ability & \(\mathsf{X}\) Performance is impacted by adverse weather conditions & Integration with image-based sensing modalities & Surveillance \\ \cline{1-1} \cline{3-4}  & Relatively high accuracy & \(\mathsf{X}\) Vulnerable to visual illusions & Power consumption & Environmental monitoring \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Assessment of vision-based UAV navigation methods. * PSO: the optimal path for particles (i.e., drones with a swarm) can be attained by means of a competition strategy-based PSO, after comparison between the current global path with respect to other global candidates [112]. * ACO: the premature convergence of a single-colony ACO algorithm can be overcome using multi-colony ACO, where multiple UAV groups search for the optimal routes to the destination [113]. * GA: the 3D position of a UAV is encoded into a chromosome which in turn contains information about the UAV's position/motion (e.g., acceleration, rate of the climbing angle, rate of the heading angle, etc.). The present-time 3D coordinates are obtained from the chromosome decoding and then evaluated by a fitness function. Eventually, path selection and information loss/exchange are referred to genetic operations [114]. * DE: in the case of a disaster (i.e., the navigation becomes harder), a constraint DE converges toward the optimum UAV route by selecting the high fitness values and minimum constraint violations among all probable traveling points [115]. * GWO: for fast convergence and efficient environmental exploitation, the conventional GWO can be hybridized with other algorithms (e.g., modified symbiotic organisms search), eventually yielding better UAV path navigation [116]. Each of the algorithms presented in Table 2 is nature-inspired, and they all aim to efficiently explore the space of available solutions to achieve the best/optimal solution (i.e., in this case, the best navigation route). Additionally, they utilize a population-based approach, such that multiple candidate individuals (e.g., swarms in the case of PSO) are iteratively improved.