rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Minimalist Multi-Robot Clustering of Square Objects
    sub title: New Strategies, Experiments, and Analysis
  name:
    name part: Song, Yong
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Shell, Dylan (Dylan A.)
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Studies of minimalist multi-robot systems consider multiple robotic agents, each with limited individual capabilities, but with the capacity for self-organization in order to collectively perform coordinated tasks. Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Our work considers a variation of an object clustering derived from the influential ant-inspired work of Beckers, Holland and Deneubourg which proposed stigmergy as a design principle for such multi-robot systems. Since puck mechanics contribute to cluster accrual dynamics, we studied a new scenario with square objects because these pucks into clusters differently from cylindrical ones. 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 behaviours, Twisting and Digging, which exploit the objects' geometry to pry boxes free from boundaries. Physical robot experiments illustrate that cooperation between twisters and diggers can succeed in forming a single central cluster. 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. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149502
  note: by Yong Song.
  note: "Major Subject: Computer Science"
  note: Includes vita.
  note: Master of Science Texas A & M University 2013 http://hdl.handle.net/1969.1/149502
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Computer Science
  subject:
    topic: Multi-robot system
  subject:
    topic: Object clustering
  subject:
    topic: Square objects
  identifier: http://hdl.handle.net/1969.1/149502
  record info:
    record content source: TXA
    record creation date: 140117
    record change date: 20151206091203.0
    record identifier: in00003426008
    language of cataloging:
      language term: eng