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Self-organized Clustering of Square Objects by Multiple Robots

  • Yong Song
  • Jung-Hwan Kim
  • Dylan A. Shell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)

Abstract

Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes —an especially challenging instance since flat edges exacerbate adhesion to boundaries— using simpler robots than previous published research. Our solution consists of two novel behaviors, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance.  Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.

Keywords

Central Cluster Mixed Strategy Multiple Robot Object Cluster Digging Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yong Song
    • 1
  • Jung-Hwan Kim
    • 1
  • Dylan A. Shell
    • 1
  1. 1.Dept. of Computer Science and EngineeringTexas A&M UniversityUSA

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