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Reconfiguring Massive Particle Swarms with Limited, Global Control

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Algorithms for Sensor Systems (ALGOSENSORS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 8243))

Abstract

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal such as gravity or a magnetic field. Upon activation of the field, each particle moves maximally in the same direction, until it hits a stationary obstacle or another stationary particle. In an open workspace, this system model is of limited use because it has only two controllable degrees of freedom—all particles receive the same inputs and move uniformly. We show that adding a maze of obstacles to the environment can make the system drastically more complex but also more useful. The resulting model matches ThinkFun’s Tilt puzzle.

If we are given a fixed set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration can be transformed into a desired target configuration. On the positive side, we provide constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations.

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Acknowledgments

We acknowledge the helpful discussion and motivating experimental efforts with T. pyriformis cells by Yan Ou and Agung Julius at RPI and Paul Kim and MinJun Kim at Drexel University. This work was supported by the National Science Foundation under CPS-1035716.

Author information

Authors and Affiliations

  1. Department of Computer Science, Rice University, Houston, TX, 77005, USA

    Aaron Becker, Golnaz Habibi & James McLurkin

  2. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, 02139, USA

    Erik D. Demaine

  3. Department of Computer Science, TU Braunschweig, Mühlenpfordtstr. 23, 38106, Braunschweig, Germany

    Sándor P. Fekete

Authors
  1. Aaron Becker
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  2. Erik D. Demaine
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  3. Sándor P. Fekete
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  4. Golnaz Habibi
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  5. James McLurkin
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Corresponding author

Correspondence to Sándor P. Fekete .

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Editors and Affiliations

  1. University of Ottawa, Ottawa, Ontario, Canada

    Paola Flocchini

  2. Stony Brook University, Stonybrook, New York, USA

    Jie Gao

  3. School of Computer Science, Carleton University, Ottawa, Ontario, Canada

    Evangelos Kranakis

  4. University of Paderborn, Paderborn, Germany

    Friedhelm Meyer auf der Heide

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Becker, A., Demaine, E.D., Fekete, S.P., Habibi, G., McLurkin, J. (2014). Reconfiguring Massive Particle Swarms with Limited, Global Control. In: Flocchini, P., Gao, J., Kranakis, E., Meyer auf der Heide, F. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2013. Lecture Notes in Computer Science(), vol 8243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45346-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-45346-5_5

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