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Programmable self-assembly of chained modules holds potential for the automatic shape formation of morphologically adapted robots. However, current systems are limited to modules of uniform rigidity, which restricts the range of obtainable morphologies and thus the functionalities of the system. To address these challenges, we previously introduced soft cells as modules that can obtain different mechanical softness pre-setting. We showed that such a system can obtain a higher diversity of morphologies compared to state-of-the-art systems and we illustrated the system’s potential by demonstrating the self-assembly of complex morphologies. In this paper, we extend our previous work and present an automatic method that exploits our system’s capabilities in order to find a linear chain of soft cells that self-folds into a target 2-D shape.
Posted on: May 19, 2014
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Programmable self-assembly of chained robotic systems holds potential for the automatic construction of complex robots from a minimal set of building blocks. However, current robotic platforms are limited to modules of uniform rigidity, which results in a limited range of obtainable morphologies and thus functionalities of the system. To address these challenges, we investigate in this paper the role of softness in a programmed self-assembling chain system. We rely on a model system consisting of “soft cells” as modules that can obtain different mechanical softness presettings. Starting from a linear chain configuration, the system self-folds into a target morphology based on the intercellular interactions. We systematically investigate the influence of mechanical softness of the individual cells on the self-assembly process. Also, we test the hypothesis that a mixed distribution of cells of different softness enhances the diversity of achievable morphologies at a given resolution compared to systems with modules of uniform rigidity. Finally, we illustrate the potential of our system by the programmable self-assembly of complex and curvilinear morphologies that state-of-the-art systems can only achieve by significantly increasing their number of modules.
Posted on: June 23, 2014
