Swarm Intelligence

, Volume 9, Issue 1, pp 1–22 | Cite as

The TAM: abstracting complex tasks in swarm robotics research

  • Arne Brutschy
  • Lorenzo Garattoni
  • Manuele Brambilla
  • Gianpiero Francesca
  • Giovanni Pini
  • Marco Dorigo
  • Mauro Birattari


Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations—no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in swarm robotics research. This approach is based on a physical device called the “task abstraction module” (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck—research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a swarm of e-puck robots.


Swarm robotics Task abstraction Swarm intelligence Robotics 



The authors would like to thank Álvaro Gutiérrez and Manuel Castillo-Cagigal for their help with designing the electronics of the TAM. The research leading to the results presented in this paper has received funding through the ERC Advanced Grant “E-SWARM: Engineering Swarm Intelligence Systems” (ERC Grant Agreement No. 246939). Arne Brutschy, Manuele Brambilla, Marco Dorigo, and Mauro Birattari acknowledge support from the Belgian F.R.S.—FNRS of Belgium’s Wallonia-Brussels Federation.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Arne Brutschy
    • 1
  • Lorenzo Garattoni
    • 1
  • Manuele Brambilla
    • 1
  • Gianpiero Francesca
    • 1
  • Giovanni Pini
    • 1
  • Marco Dorigo
    • 1
  • Mauro Birattari
    • 1
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

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