Skip to main content
Log in

The TAM: abstracting complex tasks in swarm robotics research

  • Published:
Swarm Intelligence Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. The e-puck is a small, round mobile robot designed for research purposes by Mondada et al. (2009).

  2. http://www.e-puck.org/.

  3. http://www.arduino.cc/.

  4. http://gna.org/projects/e-puck/.

  5. It should be noted that, although the semantics of activity diagrams is loosely based on Petri nets, activity diagrams are unsuitable for simulation because “the rules for activity execution are not clearly explained and defined in the UML specification” (Spiteri Staines 2010).

  6. By convention, the places of a Petri net can be omitted in order to visualize better the structure of the net (Petri and Reisig 2008). The full version of the Petri net and instructions for simulating it can be found in the supplementary online material (Brutschy et al. 2013).

  7. http://www.swarm-bots.org/.

  8. http://www.swarmanoid.org/.

  9. See http://youtu.be/M2nn1X9Xlps for a movie describing the swarm and its task.

References

  • Acerbi, A., Marocco, D., & Nolfi, S. (2007). Social facilitation on the development of foraging behaviors in a population of autonomous robots. In F. Almeida e Costa, L. Rocha, E. Costa, I. Harvey, & A. Coutinho (Eds.), Advances in artificial life (Vol. 4648, pp. 625–634)., Lecture notes in computer science Berlin: Springer.

    Chapter  Google Scholar 

  • Banzi, M. (2008). Getting started with Arduino. Sebastopol, CA: O’Reilly Media.

    Google Scholar 

  • Beni, G. (2005). From swarm intelligence to swarm robotics. In E. Şahin & W. M. Spears (Eds.), Swarm robotics (Vol. 3342, pp. 1–9)., Lecture notes in computer science Berlin: Springer.

    Chapter  Google Scholar 

  • Berman, S., Halász, A., Hsieh, M. A., & Kumar, V. (2009). Optimized stochastic policies for task allocation in swarms of robots. IEEE Transactions on Robotics, 25(4), 927–937.

    Article  Google Scholar 

  • Brambilla, M., Brutschy, A., Dorigo, M., & Birattari, M. (2014). Property-driven design for robot swarms: A design method based on prescriptive modeling and model checking. ACM Transactions on Autonomous and Adaptive Systems, 9(4), 17:1–17:28.

  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.

    Article  Google Scholar 

  • Brutschy, A. (2014). The TAM: A device for task abstraction in swarm robotics research. Technical Report TR/IRIDIA/2010-015.005, Belgium: IRIDIA, Université Libre de Bruxelles.

  • Brutschy, A., Garattoni, L., Brambilla, M., Francesca, G., Pini, G., Dorigo, M., & Birattari, M. (2013). The TAM: Abstracting complex tasks in swarm robotics research—supplementary online material. Retrieved from http://iridia.ulb.ac.be/supp/IridiaSupp2012-002/.

  • Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., & Dorigo, M. (2014). Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Autonomous Agents and Multi-Agent Systems, 28(1), 101–125.

    Article  Google Scholar 

  • Brutschy, A., Tran, N.-L., Baiboun, N., Frison, M., Pini, G., Roli, A., et al. (2012). Costs and benefits of behavioral specialization. Robotics and Autonomous Systems, 60(11), 1408–1420.

    Article  Google Scholar 

  • Campo, A., Garnier, S., Dédriche, O., Zekkri, M., & Dorigo, M. (2011). Self-organized discrimination of resources. PLoS One, 6(5), e19888.

    Article  Google Scholar 

  • Castillo-Cagigal, M., Brutschy, A., Gutiérrez, Á., & Birattari, M. (2014). Temporal task allocation in periodic environments: An approach based on synchronization (Vol. 8667). InProceedings of the 9th International Conference on Swarm Intelligence (ANTS’14) (pp. 182–193). Lecture Notes in Computer Science Berlin/Heidelberg, Germany: Springer.

  • Donald, B. R., Jennings, J., & Rus, D. (1997). Information invariants for distributed manipulation. The International Journal of Robotics Research, 16(5), 673–702.

    Article  Google Scholar 

  • Dorigo, M., Birattari, M., & Brambilla, M. (2014). Swarm robotics. Scholarpedia, 9(1), 1463.

    Article  Google Scholar 

  • Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nolfi, S., Baaboura, T., et al. (2013). Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, 20(4), 60–71.

    Article  Google Scholar 

  • Fontan, M. S., & Matarić, M. J. (1996). A study of territoriality: The role of critical mass in adaptive task division. In P. Maes, M. J. Matarić, J.-A. Meyer, J. Pollack, & S. Wilson (Eds.), From animals to animats 4: Proceedings of the Fourth International Conference of Simulation of Adaptive Behavior (pp. 553–561). Cambridge, MA: MIT Press.

  • Francesca, G., Brambilla, M., Brutschy, A., Garattoni, L., Miletitch, R., Podevijn, G., et al. (2014a). An experiment in automatic design of robot swarms: AutoMoDe-Vanilla, EvoStick, and human experts (Vol. 8667). In M. Dorigo, M. Birattari, S. Garnier, H. H. M. M. de Oca, C. Solnon, & T. Stützle (Eds.), Proceedings of the 9th International Conference on Swarm Intelligence (ANTS’14) (pp. 25–37). Lecture Notes in Computer Science, Springer: Berlin/Heidelberg, Germany.

  • Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., & Birattari, M. (2014b). AutoMoDe: A novel approach to the automatic design of control software for robot swarms. Swarm Intelligence, 8(2), 89–112.

    Article  Google Scholar 

  • Gauci, M., Chen, J., Li, W., Dodd, T. J., & Groß, R. (2014). Self-organized aggregation without computation. The International Journal of Robotics Research, 33(8), 1145–1161.

    Article  Google Scholar 

  • Goldberg, D., & Matarić, M. J. (2002). Design and evaluation of robust behavior-based controllers. In T. Balch & L. E. Parker (Eds.), Robot teams: from diversity to polymorphism (pp. 315–344). Natick, MA: A. K. Peters.

    Google Scholar 

  • Groß, R., & Dorigo, M. (2009). Towards group transport by swarms of robots. International Journal of Bio-Inspired Computation, 1(1–2), 1–13.

    Article  Google Scholar 

  • Gutiérrez, A., Campo, A., Dorigo, M., Amor, D., Magdalena, L., & Monasterio-Huelin, F. (2008). An open localisation and local communication embodied sensor. Sensors, 11(8), 7545–7563.

    Article  Google Scholar 

  • Ijspeert, A. J., Martinoli, A., Billard, A., & Gambardella, L. M. (2001). Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots, 11(2), 149–171.

    Article  MATH  Google Scholar 

  • Jakobi, N., Husbands, A., & P., & A. Harvey, I., (1995). Noise and the reality gap: The use of simulation in evolutionary robotics (Vol. 929). In F. Morán, A. Moreno, J. J. Merelo, & P. Chacón (Eds.), Swarm Robotics (pp. 704–720). Advances in Artificial Life, Springer: Berlin/Heidelberg, Germany.

  • Kernbach, S., Nepomnyashchikh, V., Kancheva, T., & Kernbach, O. (2012). Specialization and generalization of robotic behavior in swarm energy foraging. Mathematical and Computer Modelling of Dynamical Systems, 18, 131–152.

    Article  MATH  Google Scholar 

  • Krieger, M. J. B., & Billeter, J.-B. (2000). The call of duty: Self-organized task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems, 30(1–2), 65–84.

    Article  Google Scholar 

  • Kube, C., & Bonabeau, E. (2000). Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(1–2), 85–101.

    Article  Google Scholar 

  • Labella, T. H., Dorigo, M., & Deneubourg, J.-L. (2006). Division of labour in a group of robots inspired by ants’ foraging behaviour. ACM Transactions on Autonomous and Adaptive Systems, 1(1), 4–25.

    Article  Google Scholar 

  • Li, L., Martinoli, A., & Abu-Mostafa, Y. S. (2004). Learning and measuring specialization in collaborative swarm systems. Adaptive Behavior, 12(3–4), 199–212.

    Article  Google Scholar 

  • Matarić, M. J., Sukhatme, G. S., & Østergaard, E. H. (2003). Multi-robot task allocation in uncertain environments. Autonomous Robots, 14, 255–263.

    Article  MATH  Google Scholar 

  • Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., et al. (2009). The e-puck, a robot designed for education in engineering. In P. J. S. Gonçalves, et al. (Eds.), Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions (pp. 59–65). IPCB: Instituto Politècnico de Castelo Branco, Portugal.

  • Nouyan, S., Groß, R., Bonani, M., Mondada, F., & Dorigo, M. (2009). Teamwork in self-organized robot colonies. IEEE Transactions on Evolutionary Computation, 13(4), 695–711.

    Article  Google Scholar 

  • Parker, L. E. (1998). Alliance: An architecture for fault tolerant multi-robot cooperation. IEEE Transactions on Robotics and Automation, 14, 220–240.

    Article  Google Scholar 

  • Petri, C. A., & Reisig, W. (2008). Petri net. Scholarpedia, 3(4), 6477.

    Article  Google Scholar 

  • Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., et al. (2012). ARGoS: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intelligence, 6(4), 271–295.

    Article  Google Scholar 

  • Pini, G., Brutschy, A., Frison, M., Roli, A., Birattari, M., & Dorigo, M. (2011). Task partitioning in swarms of robots: An adaptive method for strategy selection. Swarm Intelligence, 5(3–4), 283–304.

    Article  Google Scholar 

  • Pini, G., Brutschy, A., Scheidler, A., Dorigo, M., & Birattari, M. (2014). Task partitioning in a robot swarm: Retrieving objects by transferring them directly between sequential sub-tasks. Artificial Life, 20(3), 291–317.

  • Pini, G., Gagliolo, M., Brutschy, A., Dorigo, M., & Birattari, M. (2013). Task partitioning in a robot swarm: A study on the effect of communication. Swarm Intelligence, 7(2–3), 173–199.

    Article  Google Scholar 

  • Rumbaugh, J., Jacobson, I., & Booch, G. (2004). The unified modeling language reference manual (2nd ed.). Upper Saddle River, NJ: Pearson Higher Education.

    Google Scholar 

  • Sperati, V., Trianni, V., & Nolfi, S. (2008). Evolving coordinated group behaviours through maximisation of mean mutual information. Swarm Intelligence, 2(2), 73–95.

    Article  Google Scholar 

  • Spiteri Staines, A. (2010). Petri nets applications. In Intuitive transformation of UML2 activities into fundamental modeling concept petri nets and colored petri nets (pp. 673–694). Rijeka, Croatia: InTech Europe.

  • Stranieri, A., Turgut, A., Francesca, G., Reina, A., Dorigo, M., & Birattari, M. (2013). IRIDIA’s arena tracking system. Technical Report TR/IRIDIA/2013-013, Belgium: IRIDIA, Université Libre de Bruxelles.

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arne Brutschy.

Additional information

Guest editor: Roderich Groß.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brutschy, A., Garattoni, L., Brambilla, M. et al. The TAM: abstracting complex tasks in swarm robotics research. Swarm Intell 9, 1–22 (2015). https://doi.org/10.1007/s11721-014-0102-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11721-014-0102-6

Keywords

Navigation