## 1 Introduction
### Importance of the Study  
In general, UAV path planning methods fall into three categories, as follows:  
1. Short distances with high agility (e.g., in cluttered indoor areas [35]), for which dynamics-aware kinematic (kinodynamic) trajectory is required. This ensures the dynamic feasibility of motions in relation to the dynamics of quadrotors [36]. Normally, control signals are generated by the trajectory planning algorithm [37], in which the dynamics and kinematics of the UAV are taken into account. 2. Medium distance with a medium level of agility (e.g., urban areas), in which kinematic trajectory planning is considered by focusing on motion and nonholonomic (i.e., rate of heading change) constraints [38]. 3. Long-distance sparse environment with no stress on agility and obstacles [39], in which only an optimal energy-efficient path is considered at a constant speed [40], which is almost the case in the wireless communication community [41; 42]. The approach taken for path planning determines the 2D coordinates of the UAV path in a global (Earth) reference frame (with a fixed altitude) where an extra controller is required to follow the trajectory. The UAV navigation for IoVs falls into the first or second class depending on the density of the environment where a kinodynamic smooth trajectory is optimally determined. Although the rotational dynamics of the UAV can be ignored, the trajectory should be smooth to be dynamically feasible in practice. Furthermore, the navigation goals and safety measures (i.e., obstacle-aware collision-free flight) must be considered. Therefore, motion and nonholonomic constraints are critical for UAV navigation, which increases the time complexity of the trajectory design problem. In this sense, the time complexity for satisfying problem constraints is prohibitively high for real-time trajectory optimization. This issue has been considered in only a few works in the wireless communication community. For instance, in addition to kinodynamic constraints in [14], the conversion efficiency of electric motors associated with quadrotors was considered in [43]. However, a generic approach is required with a linear kinodynamic model and constraints for computationally efficient and real-time trajectory optimization for autonomous navigation applications. The main difference between UAV trajectory design with path planning techniques in the robotics community is as follows. UAVs do not have a specific goal point, and the optimal trajectory varies over time depending on the IoVs' trajectories. By contrast, in most robotic navigation or autonomous vehicles, a minimum path is developed to reach a goal point. For instance, in sampling-based methods such as rapidly exploring random trees (RRT) [31; 44] and A* search [45], at each sampling, a part of the path is set on the basis of the distance between the current state and the goal considering motion constraints. However, in UAV navigation, all the possible trajectories should be explored to find the global optimal trajectory. For example, hovering without 2D horizontal movement can be a candidate for optimal trajectory. To facilitate navigation design, it is performed for a given receding time interval (e.g., 30 s), which in turn is discretized into several samples (e.g., with a sampling time of a few seconds). So, by optimizing the UAV trajectory at the initial time samples, the planner loses a degree of freedom for the final time samples to reach positions where admissible QoS can be achieved. Adopting the model predictive control (MPC) technique with a finite receding horizon [17] can be a solution, but it imposes prohibitive computational costs and preferably requires a linear kinematic model. Moreover, since the endpoint of the trajectory is uncertain (depending on the IoV trajectories and the shape of the environment), dynamic programming is also inefficient. Furthermore, the validity of LoS links should be checked for each random sample.