Bio-Inspired Agent-Based Model for Collective Shepherding

Collective shepherding is a complex problem with potentially a broad range of applications. Its complexity arises from the interaction of two collectives: 'sheep' and 'shepherds', with the latter attempting to control and guide the 'sheep'. Here, we combine an agent-based model for the 'sheep'-flock with a heuristic algorithm for the adaptive behavior of shepherds with two different behavioral modes: collecting, i.e. keeping the sheep flock together, and driving the sheep towards the target. We show that this algorithm can achieve selforganized coordination among multiple shepherds without direct communication, and investigate how the shepherding performance depends on selected parameters of the system such as sheep flock size, number of shepherds, or parameters governing the switching between the shepherd behavioral modes. We demonstrate that the algorithm can also be applied to more challenging scenarios like controlling non-cohesive or passive agents without self-propulsion. Besides extending our understanding of collective shepherding, our model provides a starting point for future research into unexplored aspects of this complex dynamical behavior.