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Steering a Particle Swarm Using Global Inputs and Swarm Statistics

Shiva Shahrokhi,Lillian Lin,Chris Ertel,Mable Wan,Aaron T. Becker

Published 2017 in arXiv.org

ABSTRACT

Microrobotics has the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions---small size and large populations---present unique challenges for controlling motion. Robotic manipulation usually assumes intelligent agents, not particle systems manipulated by a global signal. To identify the key parameters for particle manipulation, we used a collection of online games where players steer swarms of up to 500 particles to complete manipulation challenges. We recorded statistics from over ten thousand players. Inspired by techniques where human operators performed well, we investigate controllers that use only the mean and variance of the swarm. We prove the mean position is controllable and provide conditions under which variance is controllable. We next derive automatic controllers for these and a hysteresis-based switching control to regulate the first two moments of the particle distribution. Finally, we employ these controllers as primitives for an object manipulation task and implement all controllers on 100 kilobots controlled by the direction of a global light source.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv.org

  • Publication date

    2017-06-06

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    arXiv 1706.02162
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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