# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
###### Abstract  
Seeking to give unmanned aerial vehicles (UAVs) a higher level of autonomous control, this study uses edge computing systems to replace the ground control station (GCS) commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing system of the distributed architecture can give drones more flexibility in dealing with changing environmental conditions, allowing them to autonomously and instantly plan their flight path, fly in formation, or even avoid obstacles. Broadcast communications are used to realize UAV-to-UAV communications, thus allocating tasks among a swarm of UAVs and ensuring that each individual UAV collaborates as an integrated member of the group. The dynamic path programming problem for UAV swarm missions uses a two-phase tabu search with a 2-Opt exchange method and an A* search as the path programming algorithm. Distance is taken as a cost function for path programming. The turning points of no-fly zones are then increased and expanded based on drone fleet coverage, thus preventing drones from entering prohibited areas. Unlike previous work, which mostly considers only single no-fly zones, this approach accounts for multiple restricted areas, ensuring that a UAV swarm can complete its assigned task without violating no-fly zones. A drone encountering an obstacle while traveling along the route set by the algorithm will update the map information in real time, allowing for instant recharting of the optimal path to the goal as a reverse search using the D* Lite algorithm. U 2022 2022 2022 20222 20  
A centralized control system is composed of a ground control station (GCS), which holds environment and map data, as well as information about the status of all UAVs. Algorithms assign mission tasks to each UAV through the ground control station, with instructions issued via communications equipment to each UAV controller for coordinated task execution. Ground station control is the most common system architecture for controlling autonomous UAV flight. The advantage of this architecture is that the ground station can calculate algorithms and assign tasks using a high-performance computer. Previously, in the authors' research laboratory, an open source Mission Planner GCS for swarming multiple UAV technology was developed with great success [4; 5]. However, the execution of such swarm missions is overly dependent on ground station guidance and control; thus, the mission range is restricted by the effective communication distance between the drone and the ground station. Drone flight is controlled through the central terminal, leaving individual aircraft unable to adapt quickly to unexpected conditions, such as obstacles. Distributed control is a solution that addresses these drawbacks in centralized control systems. One type of distributed control system is called an edge computing system, frequently seen in architectures supporting Internet of Things (IoT) applications. Broadly defined, an edge computing system is one in which all computing occurs outside the cloud and at the edge of the network. In recent years, the efficiency requirements of IoT devices have increased. With limited local resources, mobile edge computing has emerged as an effective solution, providing drones with more reliable line-of-sight communications and routes than ground-based vehicles [6]. This approach is easy to manage, and increased attention has focused on the use of edge computing for UAV applications [7; 8; 9]. Using an edge computing system for drones, the cloud can be regarded as a ground station that distributes all calculations to the edge (i.e., the drones). This architecture can provide a higher level of automatic control, allowing drones to make decisions autonomously through their onboard computer while also providing communication between aircraft.