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    Modeling Tensegrity Systems via Energy Minimization

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    Muresan_Adrian_201408_MSC.pdf (10.65Mb)
    Date
    2014-08-26
    Author
    Muresan, Adrian
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    Abstract
    Tensegrity systems are a type of structural system relying on a balance of tension and compression forces to maintain structural soundness. These systems have a wide variety of applications ranging from architecture to biological modeling, art, and even space exploration. This thesis provides a flexible modeling platform for tensegrity systems, allowing exploration of a wide range of systems, including fractal and adaptive tensegrity systems. In order to provide the necessary flexibility for scientific exploration, this framework incorporates a hierarchical object definition structure. A hill climbing algorithm is provided for finding minimal potential energy states of these systems. Extensive validation of the presented hill climbing algorithm shows that this algorithm finds global minima in $99\%$ of test cases. This framework employs a clear distinction between object definition, object sampling, and object optimization, to allow for a greater range of uses.
    URI for this record
    http://hdl.handle.net/1974/12380
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    • School of Computing Graduate Theses
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