Cooperative and Autonomous Flocking of Drones Using an Extended BOID Model

Drones, also called unmanned aerial vehicles (UAV), have become essential for surveillance tasks like warning systems, wildfire monitoring, and precision agriculture. To increase efficiency and energy savings, drone fleets are now prioritized over individual units. However, coordinating these fleets poses a challenge. Existing methods generally use a centralized control system for coordination and identification of events. In this paper, we introduce a cooperative and autonomous flocking method for drone piloting. This method mimics the behaviors of flocks of birds using a network coordination protocol and wireless communications. The foundation of this approach is the BOID model, which is based on three rules: separation, alignment and cohesion. Here, we improve this model by integrating detection and relative localization capabilities, resulting in the formation of dynamic and adaptable herds. The experiments carried out in simulation of our technique prove its ability to improve the performance of data exchanges and make routing more reliable, thus potentially improving the use of drones in various industries and needs.