Data-Driven Identification of Group Dynamics for Motion Prediction and Control

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 agents, and interactions between 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.
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Communication dans un congrès
6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. Springer, 42, 2007, Springer Tracts in Advanced Robotics
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Soumis le : vendredi 7 décembre 2007 - 16:54:21
Dernière modification le : vendredi 7 décembre 2007 - 17:23:41
Document(s) archivé(s) le : lundi 12 avril 2010 - 06:38:03

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  • HAL Id : inria-00194927, version 1

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Mac Schwager, Dean Anderson, Daniela Rus. Data-Driven Identification of Group Dynamics for Motion Prediction and Control. 6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. Springer, 42, 2007, Springer Tracts in Advanced Robotics. 〈inria-00194927〉

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