Beanbag Robotics: Robotic Swarms with 1-DoF Units

  • David M. M. Kriesel
  • Eugene Cheung
  • Metin Sitti
  • Hod Lipson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5217)


Robotic swarm behavior is usually demonstrated using groups of robots, in which each robot in the swarm must possess full mobile capabilities, including the ability to control both forward and reverse motion as well as directional steering. Such requirements place severe constraints on the cost and size of the individual robots (swarmers), limiting the number of units and constraining the overall minimal size of a swarm. Here we show that similarly-complex swarm behavior can be achieved using much simpler individual swarmers. These possess significantly fewer controllable degrees of freedom, namely the ability to move forward at different velocities. We demonstrate how the interaction between different units then causes the entire swarm to obtain maneuverability unavailable at the individual level. These results may open the door to fabrication of simpler and smaller units for swarms allowing significantly larger numbers of units and smaller overall swarm footprints.


Input Neuron Food Object Passive Membrane Robotic Swarm Robot Swarm 
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  1. 1.
    Camazine, S.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2003)zbMATHGoogle Scholar
  2. 2.
    Dorigo, M., Trianni, V., Şahin, E., Groß, R., Labella, T., Baldassarre, G., Nolfi, S., Deneubourg, J., Mondada, F., Floreano, D., et al.: Evolving Self-Organizing Behaviors for a Swarm-Bot. Autonomous Robots 17(2), 223–245 (2004)CrossRefGoogle Scholar
  3. 3.
    Nouyan, S., Dorigo, M.: Path formation in a robot swarm. Technical Report TR/IRIDIA/2007-002, Brussels, Belgium (February 2007)Google Scholar
  4. 4.
    Rechenberg, I.: Cybernetic Solution Path of an Experimental Problem. Farnsborough Hants: Ministery of Aviation, Royal Aircraft Establishment (1965)Google Scholar
  5. 5.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  6. 6.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65, 386–408 (1958)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kriesel, D.: A brief introduction on neural networks (2007)Google Scholar
  9. 9.
    Lipson, H.: A Relaxation Method for Simulating the Kinematics of Compound Nonlinear Mechanisms. ASME Journal of Mechanical Design 128, 719 (2006)CrossRefGoogle Scholar
  10. 10.
    Sitti, M.: Microscale and nanoscale robotics systems – Characteristics, state of the art, and grand challenges. IEEE Robotics and Automation Magazine 14(1), 53–60 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David M. M. Kriesel
    • 1
  • Eugene Cheung
    • 2
  • Metin Sitti
    • 2
  • Hod Lipson
    • 1
  1. 1.Computational Synthesis Laboratory, Mechanical and Aerospace EngineeringCornell UniversityIthaca, NYUSA
  2. 2.Robotics Institute, Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburgh, PAUSA

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