We introduce an intelligent cooperative control system for ground target tracking in a cluttered urban environment with a team of Unmanned Air Vehicles (UAVs). We extend the work of Yu et. al. [1] to add a machine learning component that uses observations of target position to learn a model of target motion. Our learner is the Sequence Memoizer [2], a Bayesian model for discrete sequence data, which we use to predict future target location identifiers, given a context of previous location identifiers. Simulated cooperative control of a team of 3 UAVs in a 100-block city filled with various sizes of buildings verifies that learning a model of target motion can improve target tracking performance.


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    Title :

    Intelligent cooperative control for urban tracking with Unmanned Air Vehicles


    Contributors:
    Cook, Kevin (author) / Bryan, Everett (author) / Yu, Huili (author) / Bai, He (author) / Seppi, Kevin (author) / Beard, Randal (author)


    Publication date :

    2013-05-01


    Size :

    418298 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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