# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 1 Introduction
### UAV Path Planning  
Another crucial issue that needs to be considered in the UAV BVLOS operations is the path planning problem. Flight safety is the most important criterion that needs to be met in a BVLOS operation. The issue of safety is mainly associated with the capability of UAVs to detect and avoid obstacles and communicate payload and CC signals with the ground control station and/or unmanned aircraft systems (UAS) traffic management (UTM) systems. In this regard, appropriate path planning and reliable wireless connectivity play vital roles in enhancing the safety of a UAV along its BVLOS mission.  
Path planning involves finding an optimal path between two points by making a tradeoff between several criteria/objects, such as traveling distance, traveling time, power consumption, and safety aspects, such as obstacle avoidance. As such, UAV path planning is considered a complicated optimization problem, in which the object is to find a superior solution (flight route) in a search space (environment) under different constraints [20].  
Generally, path planning methods that have been proposed in the literature can be categorized into three groups, as follows:  
* Global planning: Global planning intends to find a reference path before performing a mission based on the preexisting knowledge of the environment that can be provided by either users or sensors. The optimal path is considered a set of waypoints that need to be followed by the drone during its mission.
* Local planning: Local methods are used in cases where the considered environment is fully/partially unknown. Therefore, the drone needs to be equipped with onboard sensors and advanced control methods for perceiving the environment, collision avoidance, and real-time path planning. Examples of neural network/machine learning-based techniques for path planning in dynamic and unknown environments can be found in [21; 22; 23; 24].
* Hybrid approach: The hybrid strategy combines the benefits of the two methods mentioned above. In this approach, the drone follows the reference path, and if needed, it will make some amends on the path based on the received data from the environment and the current situation and position of the drone.  
The most important factors that should be considered in choosing path planning approaches are their reliability and computational complexity. Global path planning needs to be conducted prior to a flight; hence, algorithms can be run in an external processing unit. The dependability of such methods in dynamic and unpredictable situations is a major problem, even though computing complexity is not crucial for global approaches. Meanwhile, although local methods are superior in path planning in unknown and dynamic environments, they are slow in environmental mapping and decision making and computationally expensive, especially for onboard computation [25]. In addition, by relying only on local approaches, path planning will suffer from being trapped in local minima. In this case, a global path can help to get out of local minima and converge to global optima. Therefore, a combination of global and local techniques can help enhance the reliability of path planning and reduce the computational complexity in autonomous UAV operations.