Skip to main content

Advertisement

SpringerLink
Log in

Task partitioning in swarms of robots: an adaptive method for strategy selection

  • Published:
Swarm Intelligence Aims and scope Submit manuscript

Abstract

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals’ skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.

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

Similar content being viewed by others

References

  • Agassounon, W., & Martinoli, A. (2002). Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In Proceedings of the 1st international joint conference on autonomous agents and multiagent systems (pp. 1090–1097). New York: ACM Press.

    Chapter  Google Scholar 

  • Aho, A. (1983). Data structures and algorithms. Boston: Addison-Wesley.

    MATH  Google Scholar 

  • Anderson, C., & Jadin, J. L. V. (2001). The adaptive benefit of leaf transfer in Atta colombica. Insectes Sociaux, 48, 404–405.

    Article  Google Scholar 

  • Anderson, C., & Ratnieks, F. L. W. (1999). Worker allocation in insect societies: coordination of nectar foragers and nectar receivers in honey bee (Apis mellifera) colonies. Behavioral Ecology and Sociobiology, 46(2), 73–81.

    Article  Google Scholar 

  • Anderson, C., Boomsma, J. J., & Bartholdi, J. J. (2002). Task partitioning in insect societies: bucket brigades. Insectes Sociaux, 49, 171–180.

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  • Bonabeau, E., Theraulaz, G., & Deneubourg, J.-L. (1996). Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies. Proceedings of the Royal Society of London. Series B, 263(1376), 1565–1569.

    Article  Google Scholar 

  • Bonabeau, E., Sobkowski, A., Theraulaz, G., & Deneubourg, J.-L. (1999). Adaptive task allocation inspired by a model of division of labor in social insects. In D. Lundh, B. Olsson & A. Narayanan (Eds.), Bio-computing and emergent computation (pp. 36–45). Skövde: World Scientific.

    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, Université Libre de Bruxelles, Brussels, Belgium.

  • Cox, D. R., & Reid, N. (2000). The theory of the design of experiments. London: Chapman and Hall/CRC.

    MATH  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 4th 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 

  • Gordon, D. M. (1999). Interaction patterns and task allocation in ant colonies. In C. Detrain, J. M. Pasteels & J.-L. Deneubourg (Eds.), Information processing in social insects (pp. 51–67). Basel: Birkhäuser.

    Chapter  Google Scholar 

  • Hart, A. G., & Ratnieks, F. L. W. (2000). Leaf caching in Atta leafcutting ants: discrete cache formation through positive feedback. Animal Behaviour, 59(3), 587–591.

    Article  Google Scholar 

  • Hart, A. G., & Ratnieks, F. L. W. (2001a). Leaf caching in the leafcutting ant Atta colombica: organizational shift, task partitioning and making the best of a bad job. Animal Behaviour, 62(2), 227–234.

    Article  Google Scholar 

  • Hart, A. G., & Ratnieks, F. L. W. (2001b). 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 

  • Hart, A., Anderson, C., & Ratnieks, F. L. W. (2002). Task partitioning in leafcutting ants. Acta Ethologica, 5, 1–11.

    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 

  • Jeanne, R. L. (2002). Social complexity in the Hymenoptera, with special attention to the wasps. In T. Kikuchi, N. Azuma, & S. Higashi (Eds.), Proceedings of the 14th congress of the IUSSI (pp. 81–130). Sapporo: Hokkaido University Press.

    Google Scholar 

  • Kalra, N., & Martinoli, A. (2006). Comparative study of market-based and threshold-based task allocation. In M. Gini & R. Voyles (Eds.), Distributed autonomous robotic systems 7 (pp. 91–101). Tokyo: Springer.

    Chapter  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 

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

    Article  Google Scholar 

  • Lein, A., & Vaughan, R. (2008). Adaptive multi-robot bucket brigade foraging. In S. Bullock, J. Noble, R. Watson & M. A. Bedau (Eds.), Artificial life XI: proceedings of the 11th international conference on the simulation and synthesis of living systems (pp. 337–342). Cambridge: MIT Press.

    Google Scholar 

  • Lopes, J. F., Forti, L. C., Camargo, R. S., Matos, C. A. O., & Verza, S. S. (2003). The effect of trail length on task partitioning in three Acromyrmex species (Hymenoptera: Formicidae). Sociobiology, 42(1), 87–91.

    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 P. J. S. Gonçalves, P. J. D. Torres & C. M. O. Alves (Eds.), Proceedings of the 9th conference on autonomous robot systems and competitions (pp. 59–65). Castelo Branco: IPCB: Instituto Politècnico de Castelo Branco.

    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 AGENTS ’01: proceedings of the 5th international conference on autonomous agents (pp. 29–30). New York: ACM Press.

    Chapter  Google Scholar 

  • Parker, L. E. (1998). ALLIANCE: an architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation, 14(2), 220–240.

    Article  Google Scholar 

  • Parker, C. A. C., & Zhang, H. (2010). Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem. Swarm Intelligence, 4(3), 199–220.

    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., Stirling, T., Gutiérrez, A., Gambardella, L. M., & Dorigo, M. (2011). ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 5027–5034). Los Alamitos: IEEE Comput. Soc.

    Google Scholar 

  • Pini, G., Brutschy, A., Birattari, M., & Dorigo, M. (2011a). Task partitioning in swarms of robots: reducing performance losses due to interference at shared resources. In J. A. Cetto et al. (Eds.), LNEE: Vol. 85. Informatics in control, automation and robotics: selected papers from the international conference on informatics in control, automation and robotics (pp. 217–228). Berlin: Springer.

    Google Scholar 

  • Pini, G., Brutschy, A., Frison, M., Roli, A., Dorigo, M., & Birattari, M. (2011b). Task partitioning in swarms of robots: an adaptive method for strategy selection—online supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2011-003/.

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

    Article  Google Scholar 

  • Reyes, J. L., & Fernández Haeger, J. (1999). Sequential co-operative load transport in the seed-harvesting ant Messor barbarus. Insectes Sociaux, 46, 119–125.

    Article  Google Scholar 

  • Şahin, E. (2005). Swarm robotics: from sources of inspiration to domains of application. In E. Şahin & W. M. Spears (Eds.), Lecture notes in computer science: Vol. 3342. Swarm robotics (pp. 10–20). Berlin: Springer.

    Chapter  Google Scholar 

  • Scheidler, A., Merkle, D., & Middendorf, M. (2008). Stability and performance of ant queue inspired task partitioning methods. Theory in Biosciences, 127(2), 149–161.

    Article  MathSciNet  Google Scholar 

  • Seeley, T. D. (1989). Social foraging in honey bees: how nectar foragers assess their colony’s nutritional status. Behavioral Ecology and Sociobiology, 24, 181–199.

    Article  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). Pitscataway: IEEE Press.

    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 

  • Winfield, A. F. T. (2009). Towards an engineering science of robot foraging. In H. Asama, H. Kurokawa, J. Ota & K. Sekiyama (Eds.), Distributed autonomous robotic systems 8 (pp. 185–192). Berlin: Springer.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Giovanni Pini, Arne Brutschy, Marco Dorigo & Mauro Birattari

  2. DEIS-Cesena, Alma Mater Studiorum, Università di Bologna, Cesena, Italy

    Marco Frison & Andrea Roli

Authors
  1. Giovanni Pini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arne Brutschy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Frison
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Roli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Dorigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mauro Birattari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovanni Pini.

Electronic Supplementary Material

Below are the links to the electronic supplementary material.

(MP4 3.86 MB)

(MP4 26.2 MB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pini, G., Brutschy, A., Frison, M. et al. Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intell 5, 283–304 (2011). https://doi.org/10.1007/s11721-011-0060-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11721-011-0060-1

Keywords

Search

Navigation