Data‐driven identification of group dynamics for motion prediction and control

A distributed model structure for representing groups of coupled dynamic agents is proposed, and the least-squares method is used for fitting model parameters based on measured position data. The difference equation model embodies a minimalist approach, incorporating only factors essential to the movement and interaction of physical bodies. The model combines effects from an agent’s inertia, interactions between agents, and interactions between each agent and its environment. Global positioning system tracking data were collected in field experiments from a group of 3 cows and a group of 10 cows over the course of several days using custom-designed, head-mounted sensor boxes. These data are used with the least-squares method to fit the model to the cow groups. The modeling technique is shown to capture overall characteristics of the group as well as attributes of individual group members. Applications to livestock management are described, and the potential for surveillance, prediction, and control of various kinds of groups of dynamic agents are suggested. C © 2008 Wiley Periodicals, Inc.