Visually extracting the network topology of drone swarms

Drone swarms operate as decentralized systems where multiple autonomous nodes coordinate their actions through inter-drone communication. A network is a collection of interconnected nodes that communicate to share resources, with its topology representing the physical or logical arrangement of these nodes. For drone swarms, network topology plays a key role in enabling coordinated actions through effective communication links. Understanding the behavior of drone swarms requires analyzing their network topology, as it provides valuable insights into the links and nodes that define their communication patterns. The research in this paper presents a computer vision-based approach to extract and analyze the network topology of such swarms, focusing on the logical communication links rather than physical formations. Using 3D coordinates obtained via stereo vision, the method identifies communication patterns corresponding to star, ring and mesh topologies. The experimental results demonstrate that the proposed method can accurately distinguish between different communication patterns within the swarm, allowing for effective mapping of the network structure. This analysis provides practical insights into how swarm coordination emerges from communication topology and offers a foundation for optimizing swarm behavior in real-world applications.