Data-driven identification of group dynamics for motion prediciton and control

TitleData-driven identification of group dynamics for motion prediciton and control
Publication TypeJournal Article
Year of Publication2008
AuthorsSchwager M., Deweiler C., Vasilescu L., Anderson D.M., Rus D.
JournalJournal of Field Robotics
Date PublishedJune 1, 2008
ARIS Log Number225711
Keywordscontrol, data-driven, journal, Least Squares, motion prediction

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, only incorporating 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. GPS tracking data were collected in field experiments from a group of three cows and a group of ten 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 dynamical agents are suggested.