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A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems

  • Kristina Lerman
  • Alcherio Martinoli
  • Aram Galstyan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3342)

Abstract

In this paper, we review methods used for macroscopic modeling and analyzing collective behavior of swarm robotic systems. Although the behavior of an individual robot in a swarm is often characterized by an important stochastic component, the collective behavior of swarms is statistically predictable and has often a simple probabilistic description. Indeed, we show that a class of mathematical models that describe the dynamics of collective behavior can be generated using the individual robot controller as modeling blueprint. We illustrate the macroscopic modelling methods with the help of a few sample results gathered in distributed manipulation experiments (collaborative stick pulling, foraging, aggregation). We compare the models’ predictions to results of probabilistic numeric and sensor-based simulations as well as experiments with real robots. Depending on the assumptions, the metric used, and the complexity of the models, we show that it is possible to achieve quantitatively correct predictions.

Keywords

Collective Behavior Obstacle Avoidance Real Robot Finite State Automaton Swarm Size 
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 2005

Authors and Affiliations

  • Kristina Lerman
    • 1
  • Alcherio Martinoli
    • 2
  • Aram Galstyan
    • 1
  1. 1.USC Information Sciences InstituteMarina del ReyUSA
  2. 2.Swarm-Intelligent Systems GroupNonlinear Systems Laboratory, EPFLLausanneSwitzerland

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