Conference Paper

Modeling Phase Transition in Self-organized Mobile Robot Flocks.

Conference: Ant Colony Optimization and Swarm Intelligence, 6th International Conference, ANTS 2008, Brussels, Belgium, September 22-24, 2008. Proceedings
Source: DBLP
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    ABSTRACT: In this paper, we study how and to what extent a self-organized mobile robot flock can be guided to move in a desired direction by informing some of the individuals within the flock. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space, avoiding obstacles. In its original form, the flock does not have a preferred direction and would wander aimlessly. In this study, we extend this behavior by "informing" some of the individuals about the desired direction that we wish the swarm to move. The informed robots do not signal that they are "informed" (a.k.a. unacknowledged leadership) and instead guide the swarm by their tendency to move in the desired direction. Through experimental results with physical and simulated robots we show that the self-organized flocking of a robot swarm can be effectively guided by an informed minority. We use two metrics to measure the accuracy of the flock direction, and the ability to stay cohesive. We show that the accuracy of guidance increases with 1)the flock size, 2)the "stubbornness" of the informed individuals to align with the desired direction, and 3)the ratio of the informed individuals.
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    ABSTRACT: Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
    Swarm Intelligence 03/2013; 7:1-41. DOI:10.1007/s11721-012-0075-2 · 0.64 Impact Factor
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    ABSTRACT: In this paper, we study how a self-organized mobile robot flock can be steered toward a desired direction through externally guiding some of its members. Specifically, we propose a behavior by extending a previously developed flocking behavior to steer self-organized flocks in both physical and simulated mobile robots. We quantitatively measure the performance of the proposed behavior under different parameter settings using three metrics, namely, (1) the mutual information metric, adopted from Information Theory, to measure the information shared between the individuals during steering, (2) the accuracy metric from directional statistics to measure the angular deviation of the direction of the flock from the desired direction, and (3) the ratio of the largest aggregate to the whole flock and the ratio of informed individuals remaining with the largest aggregate, as a metric of flock cohesion. We conducted a systematic set of experiments using both physical and simulated robots, analyzed the transient and steady-state characteristics of steered flocking, and evaluate the parameter conditions under which a swarm can be successfully steered. We show that the experimental results are qualitatively in accordance with the ones that were predicted in Couzin etal. model (Nature, 433:513–516, 2005) and relate the quantitative differences to the differences between the models. KeywordsSwarm robotics-Self-organization-Flocking
    Neural Computing and Applications 09/2010; 19(6):849-865. DOI:10.1007/s00521-010-0355-y · 1.76 Impact Factor

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