Multi-agent systems in nature are able to self-organise in order to efficiently achieve coordinated motion and control in the presence of uncertainties and obstacles. Examples include flocking behaviour in birds, schooling in fish groups and animal migration; where the coordinated motion is often an emerging feature of the group of mobile agents achieved via local interactions, with each agent observing only the motion of its neighbours. When described in terms of a dynamic network of interacting agents, we observe that network topology is rewired in order for the swarm, school or flock to negotiate obstacles or interact with other species (e.g. predators). Moreover network evolution is exploited to achieve resilient and fault-tolerant formations, able to maintain their function and coherence even in the presence of sudden environmental changes or the death/loss of some agents and/or interconnections.
Models of collective animal motion can greatly aid in the design and interpretation of behavioural experiments that seek to unravel, isolate, and manipulate the determinants of leader-follower relationships. Here, we develop an initial model of zebrafish social behaviour, which accounts for both speed and angular velocity regulatory interactions among conspecifics. Using this model, we analyse the macroscopic observables of small shoals influenced by an `informed' agent, showing that leaders which actively modulate their speed with respect to their neighbours can entrain and stabilise collective dynamics of the naive shoal.
