UAV Swarm Real-Time Rerouting by Edge Computing under a Changing Environment

Abstract

To allow UAVs to equip a higher level of autonomous control, this research uses edge computing systems to replace the ground control station commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing system of the distributed architecture gives the 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 for allocating tasks among a swarm of UAVs and ensuring each individual collaborates as an integrated member of the group. The dynamic path programming problem for the UAV swarm mission uses a 2-phase Tabu search with the 2-Opt exchange method and A* search as the path programming algorithm. Distance is taken as a cost function for path programming. We then increase and expand the turning-points of no-fly zones based on drone fleet coverage, thus preventing drones from entering prohibited areas. Whereas previous work mostly only considers 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 an instant recharting of the optimal path to the goal as a reverse search using the D* Lite algorithm.