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Morphogenetic Engineering pp 157–188Cite as

Programming Self-Assembling Systems via Physically Encoded Information

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Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Throughout nature, in both the inorganic and organic realms, complex entities emerge as a result of self-assembly from decentralised components governed by simple rules. Natural self-assembly is dictated by the morphology of the components and the environmental conditions they are subjected to, as well as the physical and chemical properties of their components and environments—their information. Components, their environment, and the interactions among them form a system, which can be described as a set of simple rules. The process of self-assembly is equivalent to a physical computation, through the interaction and transformation of physically and chemically encoded information. A critical question then arises: How does one program self-assembling systems? In the pursuit of answering this question we first use the classic Penrose physical self-assembling system as a case study. This is followed by our three-level design approach, which comprises: specifying a set of self-assembly rules, modelling these rules to determine the outcome of a system in software, and translating to a physical system by mapping the set of rules using physically encoded information. The objective of our approach is to provide a bottom-up design methodology to create scalable self-assembling systems. Two physically encoded information schemes are presented in eight experiments, to demonstrate how component interactions can be directed during the self-assembly process. The results show that the translated physical systems self-assembled into the target structures achieved by the modelling. We use rapid prototyping to construct the translated components and their environment in the latter three experiments. These successful results demonstrate how our three-level approach can be used for programming self-assembling systems via physically encoded information.

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Notes

  1. 1.

    Supplementary resources, including CNC/CAD files [20] and videos, pertaining to the case study and experiments can be found at http://www.navneetbhalla.com/resources.

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Acknowledgments

We thank Charanjit Bilkhu and Luc Martin at Surtek Precision Machining Inc. for their assistance in constructing parts for the Penrose system. We also thank Garry Campbell and his team at Nova Product Development Services Ltd., and Craig LeBlanc and Christopher Massie at the Faculty of Environmental Design, University of Calgary, for their assistance in constructing parts for the three experiments.

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Authors and Affiliations

  1. Swarm Intelligence Group, Department of Computer Science, University of Paderborn, 33102 , Paderborn, Germany

    Navneet Bhalla

  2. Department of Computer Science, University College London, London, WC1E 6BT, UK

    Peter J. Bentley

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  1. Navneet Bhalla
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  2. Peter J. Bentley
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Correspondence to Navneet Bhalla .

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Editors and Affiliations

  1. Institut des Systèmes Complexes, Paris Ile-de-France, Ecole Polytechnique, rue Lhomond 57-59, Paris, 75005, France

    René Doursat

  2. Binghamton University, Dept. Bioengineering, State University of New York, Binghamton, 13902-6000, New York, USA

    Hiroki Sayama

  3. Faculté des Sciences et Technologie, Lab. LACL-EA 4219, Université Paris-Est Créteil, avenue due Général de Gaulle 61, Créteil Cedex, 94010, France

    Olivier Michel

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Bhalla, N., Bentley, P.J. (2012). Programming Self-Assembling Systems via Physically Encoded Information. In: Doursat, R., Sayama, H., Michel, O. (eds) Morphogenetic Engineering. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33902-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-33902-8_7

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