Controlling Diversity to Maximize Performance in a Heterogeneous Swarm of Robots
Amanda Prorok, M. Ani Hsieh, Vijay Kumar

Citation
Amanda Prorok, M. Ani Hsieh, Vijay Kumar. "Controlling Diversity to Maximize Performance in a Heterogeneous Swarm of Robots". Talk or presentation, 14, October, 2015; Poster presented at the 2015 TerraSwarm Annual Meeting.

Abstract
We are interested in a principled study of the effects of diversity in heterogeneous swarms. In order to evaluate the implications of heterogeneity on performance, we consider the concrete problem of distributing a large group of robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. We develop a continuous model of the system at a macroscopic level, and formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. In order to evaluate the effects of heterogeneity, we propose a diversity metric that defines the notion of eigenspecies. We show that our metric correlates with performance: the higher the cardinality of the eigenspecies, the harder it becomes to optimize the system. Our approach is validated over multiple levels of abstraction, and real robot results confirm its validity on physical platforms.

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  • HTML
    Amanda Prorok, M. Ani Hsieh, Vijay Kumar. <a
    href="http://www.terraswarm.org/pubs/642.html"><i>Controlling
    Diversity to Maximize Performance in a Heterogeneous Swarm
    of Robots</i></a>, Talk or presentation,  14,
    October, 2015; Poster presented at the <a
    href="http://terraswarm.org/conferences/15/annual"
    >2015 TerraSwarm Annual Meeting</a>.
  • Plain text
    Amanda Prorok, M. Ani Hsieh, Vijay Kumar. "Controlling
    Diversity to Maximize Performance in a Heterogeneous Swarm
    of Robots". Talk or presentation,  14, October, 2015;
    Poster presented at the <a
    href="http://terraswarm.org/conferences/15/annual"
    >2015 TerraSwarm Annual Meeting</a>.
  • BibTeX
    @presentation{ProrokHsiehKumar15_ControllingDiversityToMaximizePerformanceInHeterogeneous,
        author = {Amanda Prorok and M. Ani Hsieh and Vijay Kumar},
        title = {Controlling Diversity to Maximize Performance in a
                  Heterogeneous Swarm of Robots},
        day = {14},
        month = {October},
        year = {2015},
        note = {Poster presented at the <a
                  href="http://terraswarm.org/conferences/15/annual"
                  >2015 TerraSwarm Annual Meeting</a>},
        abstract = {We are interested in a principled study of the
                  effects of diversity in heterogeneous swarms. In
                  order to evaluate the implications of
                  heterogeneity on performance, we consider the
                  concrete problem of distributing a large group of
                  robots among a set of tasks that require
                  specialized capabilities in order to be completed.
                  We model the system of heterogeneous robots as a
                  community of species, where each species (robot
                  type) is defined by the traits (capabilities) that
                  it owns. We develop a continuous model of the
                  system at a macroscopic level, and formulate an
                  optimization problem that produces an optimal set
                  of transition rates for each species, so that the
                  desired trait distribution is reached as quickly
                  as possible. In order to evaluate the effects of
                  heterogeneity, we propose a diversity metric that
                  defines the notion of eigenspecies. We show that
                  our metric correlates with performance: the higher
                  the cardinality of the eigenspecies, the harder it
                  becomes to optimize the system. Our approach is
                  validated over multiple levels of abstraction, and
                  real robot results confirm its validity on
                  physical platforms.},
        URL = {http://terraswarm.org/pubs/642.html}
    }
    

Posted by Amanda Prorok, PhD on 7 Oct 2015.
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