## 4 Comparison
### Comparison of Trajectory Planning Algorithms  
Following the studied scheme in Section 3.1, this subsection demonstrates a relative comparison among the different trajectory strategies taken by the authors in terms of main idea with their respective advantages, limitations, evaluated metrics, performance objective and innovative ideas. Table 2 lists the drone-based delivery techniques that focus on the trajectory design with their respective advantages and limitations. Based on each trajectory scheme discussed earlier for the drone-based delivery systems, most studies focus on either maximizing the flight time to ensure maximum coverage in a single charge or minimizing the traveling distance for delivery. Many of the algorithmic approaches resort to heuristic approaches in the problem formulation. However, machine learning-based approaches are promising performance despite being computationally expensive, which allows for their practical implementation. Despite proving the optimal flight path, none of these schemes considered the impact of weather and various wind conditions on the trajectory formulation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Main Idea** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{A-Ptr-Net [6]} & Drone-based automated trajectory design for optimal delivery path. & Adaptability to new trajectory data regardless of explicit distance matrix. Takes non-linear energy consumption, customer demand and service timeframe into account while calculating optimal route. & Requires large data volume for accurate drone trajectory prediction. \\ \hline \multirow{4}{*}{JRCS [12]} & For long-haul drone-based delivery mission, simultaneous route and CS location is devised to maximize the safe flight coverage. & This scheme provides maximum delivery coverage (distance) while utilizing the well-placed CSs across the area in a single mission. & Proposed scheme ignores the effect of weather as well as any obstacle that the drone might face along its trajectory. \\ \hline \multirow{4}{*}{CRP-TD [15]} & A collaboration of truck-drone combination to solve the preassigned delivery problem & The algorithm integrates the K-means clustering and the nearest neighbor strategy to generate an initial solution which helps to reach optimal solutions quickly and accurately. & Stochastic demand for the delivery schedule is not taken into design consideration. \\ \hline \multirow{4}{*}{EHTDDP [16]} & Formulates a combined truck-drone delivery problem which is addressed by MIP and heuristic approach & The hybrid heuristic based on general variable neighborhood search metaheuristics is able to obtain high-quality solutions for large-size instances with an efficiency rate over 80\%. & Only single drone-based delivery scenario is considered. Moreover, the proposed heuristic approach does not guarantee optimal solution \\ \hline \multirow{4}{*}{FSTSP-STT [41]} & Flight path of the drone is formulated as MDP and minimizing the flight time is prioritized. & This model incorporates stochastic traffic conditions and works with artificially generated data to demonstrate the algorithmic working efficiency. Moreover, real-time data were also used for further validation. & Proposed approach demonstrates higher computational time and flight data than other benchmarks. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of trajectory planning algorithms in drone routing in terms of main idea, advantages, and limitations. Table 3 compares the different studied schemes for trajectory technique of drone-based delivery systems in terms of the evaluation metrics, performance objectives, and innovative features. Each of these techniques is evaluated with their respective target parameters, as discussed in Table 3. However, each technique has their uniqueness either in terms of algorithmic solution approaches or formulating the desired objective in a meaningful way.