Modeling Tensegrity Systems via Energy Minimization

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.