A Multi-Layer Swarm Control Model for Information Propagation and Multi-Tasking

While individuals in natural swarms are collectively performing complex tasks such as foraging or synchronization, critical information such as predator warnings propagate across the swarm almost instantly and presumably without explicit communication between the individuals. In this paper, we propose a multi-layer control model composed of an interplay of decentralized algorithms for perception and swarming. Through this novel model, we demonstrate implicit information propagation and multi-tasking in swarms using only local interactions and without explicit communication or prescribed formations. For a complete graph, we prove that variations on individual speed are implicitly propagated across the swarm, which causes the swarm to turn almost instantly. Additionally, we prove that the spatial variances of the shape of the swarm are bounded, which implicitly ensures the connectivity of the graph. Finally, we provide various simulation results demonstrating the effectiveness of the model for swarms with complete and non-complete graphs performing collective synchronization and source seeking while avoiding a predator. The proposed model has the potential to be used in various applications such as designing tactics for a swarm of drones to avoid or chase a malicious agent.