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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #214254
Title: Data-driven identification of group dynamics for motion prediction and control

Author
item SCHWAGER, MAC - MIT
item Anderson, Dean
item RUS, DANIELA - MIT

Submitted to: Laboratory Automation Robotics International Symposium
Publication Type: Proceedings
Publication Acceptance Date: 6/9/2007
Publication Date: N/A
Citation: N/A

Interpretive Summary: The location of free-ranging cattle on an arid landscape, The location of free-ranging cattle on an arid landscape, determined using global positioning system (GPS) hardware, was used to evaluate a decentralized physically motivated difference equation model that employed the Least Squares method to fit model parameters. This statistical approach was capable of showing relationships of individuals to their surroundings as well as relationships between individuals and their surroundings. The resulting model captured overall characteristics of the small group composed of three cows as well as individual animal differences. These types of statistical manipulations may prove useful, not only in the control but in the prediction of behaviors within and among dynamic agents such as free-ranging livestock.

Technical Abstract: A decentralized model structure for representing groups of coupled dynamic agents is proposed, and the Least Squares method is used for fitting model parameters based on observed position data. The physically motivated, difference equation model combines effects from agent dynamics, interactions between (probably should have been among rather than between since n=3) agents, and interactions between (probably should have been among rather than between since n=3) each agent and its environment. The technique is implemented to identify a model for a group of three cows using GPS tracking data. The model is shown to capture overall characteristics of the group as well as attributes of individual group members. Applications to surveillance, prediction, and control of various kinds of groups of dynamical agents are suggested.