Skip to main content
SpringerLink
Log in

Task partitioning in a robot swarm: a study on the effect of communication

  • Published:
Swarm Intelligence Aims and scope Submit manuscript

Abstract

Task partitioning consists in dividing a task into sub-tasks that can be tackled separately. Partitioning a task might have both positive and negative effects: On the one hand, partitioning might reduce physical interference between workers, enhance exploitation of specialization, and increase efficiency. On the other hand, partitioning may introduce overheads due to coordination requirements. As a result, whether partitioning is advantageous or not has to be evaluated on a case-by-case basis. In this paper we consider the case in which a swarm of robots must decide whether to complete a given task as an unpartitioned task, or utilize task partitioning and tackle it as a sequence of two sub-tasks. We show that the problem of selecting between the two options can be formulated as a multi-armed bandit problem and tackled with algorithms that have been proposed in the reinforcement learning literature. Additionally, we study the implications of using explicit communication between the robots to tackle the studied task partitioning problem. We consider a foraging scenario as a testbed and we perform simulation-based experiments to evaluate the behavior of the system. The results confirm that existing multi-armed bandit algorithms can be employed in the context of task partitioning. The use of communication can result in better performance, but in may also hinder the flexibility of the system.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. In the experiments, α is set to 0.5, refer to Sect. 5 for details.

  2. In our previous work, we selected for each algorithm one method for computing the threshold. In this study we use for each algorithm the corresponding method that was selected in the previous work (refer to Sect. 5).

  3. Notice that γ and η are not constants, due to their dependency on n D +n H .

  4. Each control cycle lasts 0.1 seconds.

  5. Acronym for first in, first out.

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

  7. The TAMs can communicate via WiFi. The behavior of a cache slot, that is, the coordination between two paired TAMs, can be easily implemented in the real-world by interfacing the TAMs with a PC.

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

  9. An analogous plot for Exp3 is available in Pini et al. (2012c).

References

  • Agrawal, R. (1995). Sample mean based index policies with O(logn) regret for the multi-armed bandit problem. Advances in Applied Probability, 27, 1054–1078.

    Article  MathSciNet  MATH  Google Scholar 

  • Anderson, C., & Ratnieks, F. L. W. (1999). Task partitioning in insect societies I: effect of colony size on queueing delay and colony ergonomic efficiency. The American Naturalist, 154(5), 521–535.

    Article  Google Scholar 

  • Anderson, C., & Ratnieks, F. L. W. (2000). Task partitioning in insect societies: novel situations. Insectes Sociaux, 47, 198–199.

    Article  Google Scholar 

  • Arnan, X., Ferrandiz-Rovira, M., Pladevall, C., & Rodrigo, A. (2011). Worker size-related task partitioning in the foraging strategy of a seed-harvesting ant species. Behavioral Ecology and Sociobiology, 65(10), 1881–1890.

    Article  Google Scholar 

  • Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.

    Article  MATH  Google Scholar 

  • Brutschy, A., Pini, G., Baiboun, N., Decugnière, A., & Birattari, M. (2010). The IRIDIA-TAM: A device for task abstraction for the E-puck robot. Technical Report TR/IRIDIA/2010-015, IRIDIA, ULB.

  • Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 38(2), 156–172.

    Article  Google Scholar 

  • Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning, and games. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Dorigo, M., Floreano, D., Gambardella, L. M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., Burnier, D., Campo, A., Christensen, A. L., Decugnière, A., Caro, G. D., Ducatelle, F., Ferrante, E., Förster, A., Gonzales, J. M., Guzzi, J., Longchamp, V., Magnenat, S., Mathews, N., de Oca, M. M., O’Grady, R., Pinciroli, C., Pini, G., Rétornaz, P., Roberts, J., Sperati, V., Stirling, T., Stranieri, A., Stützle, T., Trianni, V., Tuci, E., Turgut, A. E., & Vaussard, F. (2013, in press). Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine.

  • Drogoul, A., & Ferber, J. (1992). From tom thumb to the dockers: some experiments with foraging robots. In J.-A. Meyer, L. R. Herbert, & W. W. Stewart (Eds.), Proceedings of the second international conference on simulation of adaptive behavior (pp. 451–459). Cambridge: MIT Press.

    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: MIT Press.

    Google Scholar 

  • Fowler, H. G., & Robinson, S. W. (1979). Foraging by Atta sexdens (Formicidae: Attini): seasonal patterns, caste and efficiency. Ecological Entomology, 4(3), 239–247.

    Article  Google Scholar 

  • Frison, M., Tran, N.-L., Baiboun, N., Brutschy, A., Pini, G., Roli, A., Dorigo, M., & Birattari, M. (2010). Self-organized task partitioning in a swarm of robots. In M. Dorigo, M. Birattari, G. A. Di Caro, R. Doursat, A. P. Engelbrecht, D. Floreano, L. M. Gambardella, R. Groß, E. Sahin, H. Sayama, & T. Stützle (Eds.), LNCS: Vol. 6234. Swarm intelligence, 7th international conference, ANTS 2010 (pp. 287–298). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Goldberg, D., & Matarić, M. J. (2002). Design and evaluation of robust behavior-based controllers for distributed multi-robot collection tasks. In T. Balch & L. E. Parker (Eds.), Robot teams: from diversity to polymorphism (pp. 315–344). Natick: AK Peters/CRC Press.

    Google Scholar 

  • Gutiérrez, Á., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F., & Magdalena, L. (2009). Open E-puck range & bearing miniaturized board for local communication in swarm robotics. In ICRA’09: proceedings of the 2009 IEEE international conference on robotics and automation (pp. 3111–3116). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Hart, A. G., & Ratnieks, F. L. W. (2001). Task partitioning, division of labour and nest compartmentalisation collectively isolate hazardous waste in the leafcutting Atta cephalotes. Behavioral Ecology and Sociobiology, 49, 387–392.

    Article  Google Scholar 

  • Hubbell, S. P., Johnson, L. K., Stanislav, E., Wilson, B., & Fowler, H. (1980). Foraging by bucket-brigade in leaf-cutter ants. Biotropica, 12(3), 210–213.

    Article  Google Scholar 

  • Jeanne, R. L. (1986). The evolution of the organization of work in social insects. Monitore Zoologico Italiano, 20, 119–133).

    Google Scholar 

  • Lein, A., & Vaughan, R. T. (2008). Adaptive multi-robot bucket brigade foraging. In S. Bullock, J. Noble, R. Watson, & M. A. Bedau (Eds.), Artificial life XI: proceedings of the eleventh international conference on the simulation and synthesis of living systems (pp. 337–342). Cambridge: MIT Press.

    Google Scholar 

  • Lein, A., & Vaughan, R. T. (2009). Adapting to non-uniform resource distributions in robotic swarm foraging through work-site relocation. In 2009 IEEE/RSJ international conference on intelligent robots and systems (IROS’09) (pp. 601–606). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Lerman, K., & Galstyan, A. (2002). Mathematical model of foraging in a group of robots: effect of interference. Autonomous Robots, 13, 127–141.

    Article  MATH  Google Scholar 

  • Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., & Martinoli, A. (2009). The e-puck, a robot designed for education in engineering. In Proceedings of the 9th conference on autonomous robot systems and competitions (pp. 59–65). IPCB-Instituto Politecnico de Castelo Branco.

    Google Scholar 

  • Nunes, L., & Oliveira, E. (2008). Communication during learning in heterogeneous teams of learning agents. Intelligent Decision Technologies, 2(3), 153–166.

    Google Scholar 

  • Østergaard, E. H., Sukhatme, G. S., & Matarić, M. J. (2001). Emergent bucket brigading: a simple mechanisms for improving performance in multi-robot constrained-space foraging tasks. In E. Andre, S. Sen, C. Frasson, & P. M. Jörg (Eds.), AGENTS’01: proceedings of the fifth international conference on autonomous agents (pp. 29–30). New York: ACM Press.

    Chapter  Google Scholar 

  • Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: the state of the art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434.

    Article  Google Scholar 

  • Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Birattari, M., Gambardella, L. M., & Dorigo, M. (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., Birattari, M., & Dorigo, M. (2009). Task partitioning in swarms of robots: reducing performance losses due to interference at shared resources. In J. A. Cetto, J. Filipe, & J.-L. Ferrier (Eds.), LNEE: Vol. 85. Informatics in control, automation and robotics (pp. 217–228). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Pini, G., Brutschy, A., Frison, M., Roli, A., Dorigo, M., & Birattari, 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., Francesca, G., Dorigo, M., & Birattari, M. (2012a). Multi-armed bandit formulation of the task partitioning problem in swarm robotics. In M. Dorigo, M. Birattari, C. Blum, A. L. Christensen, A. P. Engelbrecht, R. Groß, & T. Stützle (Eds.), LNCS: Vol. 7461. Swarm intelligence, 8th international conference, ANTS 2012 (pp. 109–120). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Pini, G., Brutschy, A., Scheidler, A., Dorigo, M., & Birattari, M. (2012b). Task partitioning in a robot swarm: Retrieving objects by transferring them directly between sequential sub-tasks. Technical Report TR/IRIDIA/2012-010, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.

  • Pini, G., Gagliolo, M., Brutschy, A., Dorigo, M., & Birattari, M. (2012c). Task partitioning in a robot swarm: a study on the effect of communication—Online supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2012-0015/.

  • Pini, G., Brutschy, A., Pinciroli, C., Dorigo, M., & Birattari, M. (2013, in press). Autonomous task partitioning in robot foraging: an approach based on cost estimation. Adaptive behavior.

  • Ratnieks, F. L. W., & Anderson, C. (1999). Task partitioning in insect societies. Insectes Sociaux, 46(2), 95–108.

    Article  Google Scholar 

  • Schlag, K. (1998). Why imitate, and if so, how? A boundedly rational approach to multi-armed bandits. Journal of Economic Theory, 78(1), 130–156.

    Article  MathSciNet  MATH  Google Scholar 

  • Shell, D. J., & Matarić, M. J. (2006). On foraging strategies for large-scale multi-robot systems. In Proceedings of the 19th IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2717–2723). Piscataway: IEEE Press.

    Google Scholar 

  • Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365–377.

    Article  MathSciNet  MATH  Google Scholar 

  • Sutton, R., & Barto, A. (1998). Reinforcement learning, an introduction. Cambridge: MIT Press.

    Google Scholar 

  • Tan, M. (1993). Multi-agent reinforcement learning: independent vs. cooperative agents. In Proceedings of the tenth international conference on machine learning (pp. 330–337).

    Google Scholar 

  • Theraulaz, G., Bonabeau, E., Solé, R. V., Schatz, B., & Deneubourg, J.-L. (2002). Task partitioning in a ponerine ant. Journal of Theoretical Biology, 215, 481–489.

    Article  Google Scholar 

  • Weiss, G. (1999). Multiagent systems: a modern approach to distributed artificial intelligence. Cambridge: MIT Press.

    Google Scholar 

  • Whitehead, S. D. (1991). A complexity analysis of cooperative mechanisms in reinforcement learning. In T. L. Dean & K. McKeown (Eds.), Proceedings of the 9th national conference on artificial intelligence (Vol. 2, pp. 607–613). Menlo Park/Cambridge: AAAI Press/MIT Press.

    Google Scholar 

Download references

Acknowledgements

The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement n∘ 246939. Giovanni Pini acknowledges support from Université Libre de Bruxelles through the “Fonds David & Alice Van Buuren”. Arne Brutschy, Marco Dorigo, and Mauro Birattari acknowledge support from the Belgian F.R.S.–FNRS.

Author information

Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Bruxelles, Belgium

    Giovanni Pini, Arne Brutschy, Marco Dorigo & Mauro Birattari

  2. GERME and MLG, Université Libre de Bruxelles, Bruxelles, Belgium

    Matteo Gagliolo

Authors
  1. Giovanni Pini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Gagliolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arne Brutschy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Dorigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mauro Birattari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovanni Pini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pini, G., Gagliolo, M., Brutschy, A. et al. Task partitioning in a robot swarm: a study on the effect of communication. Swarm Intell 7, 173–199 (2013). https://doi.org/10.1007/s11721-013-0078-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11721-013-0078-7

Keywords

Search

Navigation