Bio-inspired 3D flocking algorithm with minimal information transfer for drones swarms

This article introduces a bio-inspired 3D flocking algorithm for a drone swarm, built upon a previously established 2D model, which has proven to be effective in promoting stability, alignment, and distance variation between agents within large groups of agents. The study highlights how the incorporation of a vertical interaction between agents and the acquisition by each agent of a minimal amount of information about their most influential neighbor impacts the collective behavior of the swarm. Additionally, we present a comprehensive investigation of the impacts of the intensity of alignment and attraction interactions on the collective motion patterns that emerge at the group level. These results, mostly conducted in a validated simulator, have significant implications for designing efficient UAV swarm systems and using collective patterns, or phases, in operational contexts such as corridor tracking, surveillance, and exploration. Further research will explore the effectiveness and efficiency of this UAV swarm flocking algorithm, as well as its ability to ensure safe transitions between collective phases in different operational contexts.