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

Mac Schwager, Carrick Detweiler, Iuliu Vasilescu, Dean M. Anderson, Daniela Rus

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)305-324
Number of pages20
JournalJournal of Field Robotics
Volume25
Issue number6-7
DOIs
StatePublished - Jun 2008
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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