2022/11/08 by Aditya Ranganathan, Ranganathan, Aditya, Alexander Heyde +6
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #FOS: Physical sciences #Modular Robots and Swarm Intelligence #Physics and Society (physics.soc-ph) #Robotics (cs.RO) #Soft Condensed Matter (cond-mat.soft)
paper · pdf · doi:10.48550/arxiv.2211.04352
openalex publication_date 2022/11/08 · openalex created_date 2022/11/15 · openalex updated_date 2026/07/28
We investigate how a shepherd should move to effectively herd a flock towards a target. Using an agent-based (ABM) and a coarse-grained (ODE) model for the flock, we pose and solve for the optimal strategy of a shepherd that must keep the flock cohesive and coerce it towards a target. Three distinct strategies emerge naturally as a function of the scaled herd size and the scaled shepherd speed: (i) mustering, where the shepherd circles the herd to ensure compactness, (ii) droving, where the shepherd chases the herd in a desired direction while sweeping back and forth, and (iii) driving, where the flock surrounds a shepherd that drives it from within. A minimal dynamical model for the size, shape, and position of the herd captures the effective behavior of the ABM and further allows us to characterize the different herding strategies in terms of the behavior of the shepherd that librates (mustering), oscillates (droving), or moves steadily (driving).