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Computing by Programmable Particles

  • Joshua J. DaymudeEmail author
  • Kristian Hinnenthal
  • Andréa W. Richa
  • Christian Scheideler
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11340)

Abstract

The vision for programmable matter is to realize a physical substance that is scalable, versatile, instantly reconfigurable, safe to handle, and robust to failures. Programmable matter could be deployed in a variety of domain spaces to address a wide gamut of problems, including applications in construction, environmental science, synthetic biology, and space exploration. However, there are considerable engineering and computational challenges that must be overcome before such a system could be implemented. Towards developing efficient algorithms for novel programmable matter behaviors, the amoebot model for self-organizing particle systems and its variant, hybrid programmable matter, provide formal computational frameworks that facilitate rigorous algorithmic research. In this chapter, we discuss distributed algorithms under these models for shape formation, shape recognition, object coating, compression, shortcut bridging, and separation in addition to some underlying algorithmic primitives.

Keywords

Programmable matter Self-organizing particle systems Distributed algorithms 

Notes

Acknowledgements

Our warmest gratitude belongs to all of our wonderful collaborators, both past and present, without whom this research would not have been possible. We would like to thank Robert Gmyr, Thim Strothmann, and Zahra Derakhshandeh for their trailblazing work on self-organizing particle systems during their Ph.D. studies. We would especially like to thank Robert for letting us use materials from his Ph.D. thesis for this chapter (in particular, his excellent images). To Dana Randall and Sarah Cannon, thank you for leading us into a new paradigm by showing us just how much one can do with a whole lot of randomness. To Irina Kostitsyna and Dorian Rudolph, thank you for all your work in developing hybrid programmable matter. Finally, to our undergraduate research assistants, especially Alexandra Porter: thank you for your enthusiasm, energy, and effort.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joshua J. Daymude
    • 1
    Email author
  • Kristian Hinnenthal
    • 2
  • Andréa W. Richa
    • 1
  • Christian Scheideler
    • 2
  1. 1.Computer Science, CIDSEArizona State UniversityTempeUSA
  2. 2.Department of Computer SciencePaderborn UniversityPaderbornGermany

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