Self-organized task allocation to sequentially interdependent tasks in swarm robotics
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In this article we present a self-organized method for allocating the individuals of a robot swarm to tasks that are sequentially interdependent. Tasks that are sequentially interdependent are common in natural and artificial systems. The proposed method does neither rely on global knowledge nor centralized components. Moreover, it does not require the robots to communicate. The method is based on the delay experienced by the robots working on one subtask when waiting for input from another subtask. We explore the capabilities of the method in different simulated environments. Additionally, we evaluate the method in a proof-of-concept experiment using real robots. We show that the method allows a swarm to reach a near-optimal allocation in the studied environments, can easily be transferred to a real robot setting, and is adaptive to changes in the properties of the tasks such as their duration. Finally, we show that the ideal setting of the parameters of the method does not depend on the properties of the environment.
KeywordsSwarm robotics Foraging Self-organization Task allocation Swarm intelligence Multi-agent systems
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 no. 246939. Marco Dorigo, Mauro Birattari, and Arne Brutschy acknowledge support from the Belgian F.R.S.–FNRS. Giovanni Pini acknowledges support from Université Libre de Bruxelles through the “Fonds David & Alice Van Buuren”.
- 1.Agassounon, W., & Martinoli, A. (2002). Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. Proceedings of the first international joint conference on autonomous agents and multi-agent systems (AAMAS-02) (pp. 1090–1097). New York: ACM Press.Google Scholar
- 7.Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Self-organized task allocation to sequentially interdependent tasks in swarm robotics—Online supplementary material. http://iridia.ulb.ac.be/supp/IridiaSupp2011-002/.
- 8.Campo, A., & Dorigo, M. (2007). Efficient multi-foraging in swarm robotics. In M. Capcarrere, A. A. Freitas, P. J. Bentley, C. G. Johnson, & J. Timmis (Eds.), Advances in artificial life: Proceedings of the VIIIth European conference on artificial life (ECAL 2005) (Vol. 4648, pp. 696–705). Berlin: Springer.Google Scholar
- 9.Christensen, A. L., O’Grady, R., & Dorigo, M. (2007). Morphology control in a multirobot system. IEEE Robotics and Automation Magazine, 11(6), 732–742.Google Scholar
- 12.Dasgupta, P. (2011). Multi-robot task allocation for performing cooperative foraging tasks in an initially unknown environment. In L. C. Jain, E. V. Aidman, & C. Abeynayake (Eds.), Innovations in defence support systems 2. Studies in computational intelligence (Vol. 338, pp. 5–20). Berlin: Springer.Google Scholar
- 14.Dorigo, M. (2005). SWARM-BOT: An experiment in swarm robotics. In P. Arabshahi & A. Martinoli (Eds.), 2005 IEEE swarm intelligence symposium (SIS-05) (pp. 192–200). Piscataway, NJ: IEEE Press.Google Scholar
- 15.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 and Automation Magazine (in press).Google Scholar
- 17.Ferreira, P. R., Boffo, F. S., & Bazzan, A. L. C. (2008). Using Swarm-GAP for distributed task allocation in complex scenarios. In N. Jamali, P. Scerri, & T. Sugawara (Eds.), Massively multi-agent technology. LNCS (Vol. 5043, pp. 107–121). Berlin: Springer.Google Scholar
- 19.Gerkey, B. P., & Matarić, M. J. (2003). Multi-robot task allocation: Analyzing the complexity and optimality of key architectures. In Proceedings of the IEEE international conference on robotics and automation (ICRA 2003) (pp. 3862–3867). Pitscataway, NJ: IEEE Press.Google Scholar
- 21.Goldberg, D., Cicirello, V., Dias, M. B., Simmons, R., Smith, S., & Stentz, A. (2003). Task allocation using a distributed market-based planning mechanism. In Proceedings of the second international joint conference on autonomous agents and multiagent systems (pp. 996–997). New York, NY: ACM Press.Google Scholar
- 23.Ikemoto, Y., Miura, T., & Asama, H. (2010). Adaptive division-of-labor control algorithm for multi-robot systems. Journal of Robotics and Mechatronics, 22(4), 514–525.Google Scholar
- 24.Kalra, N., & Martinoli, A. (2006). A comparative study of market-based and threshold-based task allocation. In Distributed autonomous robotic systems 7 (pp. 91–102). Berlin: Springer.Google Scholar
- 31.Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., et al. (2011a). ARGoS: A modular, multi-engine simulator for heterogeneous swarm robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2011) (pp. 5027–5034). Los Alamitos, CA: IEEE Computer Society Press.Google Scholar
- 32.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.Google Scholar
- 33.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.-L. Ferrier & J. Filipe (Eds.), Informatics in control, automation and robotics: Selected papers from the international conference on informatics in control, automation and robotics 2009. LNEE (Vol. 85). Berlin: Springer.Google Scholar
- 37.Theraulaz, G., Bonabeau, E., & Deneubourg, J.-L. (1998). Response threshold reinforcement and division of labour in insect societies. Proceedings: Biological Sciences, 265(1393), 327–332.Google Scholar