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SWARMORPH: Morphogenesis with Self-Assembling Robots

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Morphogenetic Engineering

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

Abstract

We detail progress towards giving robots the capacity to assemble into appropriate morphologies and to operate as a single entity when physically connected to one another. Our work is conducted on the Swarm-bot robotic platform. We develop low-level control logic to allow inter-robot connections to be formed at particular angles. We develop higher-level control logic to dictate the sequence of these connections so as to form desired morphologies. The high-level logic also allows the robots to make appropriate collective responses to different tasks. We test our morphology generation framework with a series of real-world experiments conducted on up to nine robots. We also do some experiments in a physics-based simulation environment to verify scalability.

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Notes

  1. 1.

    By contrast, the approach we present in Sect. 2.6 incorporates the possibility of the exchange of symbolic information between connected s-bots. The resulting system is able to generate arbitrary morphologies.

  2. 2.

    An s-bot cannot distinguish between LEDs belonging to the morphology to which it is currently connected and LEDs on robots in other nearby morphologies. It could therefore happen that a robot executing the Balance rule would wait for a connection slot opened by a nearby robot connected to a different morphology. In this section, we do not address this problem: we consider the formation of only one morphology at a time. However, this issue disappears with the symbolic communication based approach that we present in Sect. 2.6.

  3. 3.

    Instead of introducing symbolic communication to generate arbitrary morphologies (as we do in the next section), an alternative avenue (which we have not pursued) might be to create more morphology-extension rules of the type presented in this section, and thus generate richer morphologies whilst avoiding symbolic communication. Such a research avenue could also encompass formal analysis of the morphologically expressive power of a given set of extension rules, perhaps using a grammar based approach akin to that pursued by Klavins et al. [23].

  4. 4.

    As noted previously, to prevent damage to the robots we used a black surface instead of a gap. This surface presents sensory information to the s-bots’ ground sensors that is almost identical to the sensory information presented by the real gap obstacle.

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Acknowledgments

The research leading to the results presented in this chapter has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement Nr. 246939. Rehan O’Grady and Marco Dorigo acknowledge support from the Belgian F.R.S.-FNRS, of which they are a postdoctoral researcher and a research director, respectively.

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Authors and Affiliations

  1. IRIDIA-CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Rehan O’Grady & Marco Dorigo

  2. Instituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal

    Anders Lyhne Christensen

Authors
  1. Rehan O’Grady
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  2. Anders Lyhne Christensen
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  3. Marco Dorigo
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Correspondence to Rehan O’Grady .

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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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O’Grady, R., Christensen, A.L., Dorigo, M. (2012). SWARMORPH: Morphogenesis with Self-Assembling Robots. 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_2

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  • DOI: https://doi.org/10.1007/978-3-642-33902-8_2

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