Swarm Intelligence

, Volume 12, Issue 4, pp 307–326 | Cite as

Balancing exploitation of renewable resources by a robot swarm

  • Roman Miletitch
  • Marco Dorigo
  • Vito TrianniEmail author


Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation effort is correctly balanced is particularly challenging. Additionally, the possibility to exploit resources by multiple robots opens the way to focusing the effort either on a single or on multiple resources in parallel. This means that the swarm needs to collectively decide whether to remain cohesive or split among multiple resources, as a function of the ability of the available resources to replenish after exploitation. In this paper, we propose a decentralised strategy for a swarm of robots that adapts to the available resources and balances the effort among them, hence allowing to maximise the exploitation rate while avoiding to completely deplete the resources. A detailed analysis of the strategy parameters provides insights into the working principles and expected performance of the robot swarm.


Swarm robotics Resource exploitation Foraging Load balancing 



Vito Trianni acknowledges the support by the European Commission FP7 Programme People: Marie-Curie Actions through the project “DICE, Distributed Cognition Engineering” (Grant Agreement Number 631297). Marco Dorigo acknowledges the support from the Belgian F.R.S.-FNRS, of which he is a Research Director.

Supplementary material

11721_2018_159_MOESM1_ESM.pdf (446 kb)
Supplementary material 1 (pdf 446 KB)
11721_2018_159_MOESM2_ESM.pdf (1.9 mb)
Supplementary material 2 (pdf 1981 KB)


  1. Bailis, P., Nagpal, R., & Werfel, J. (2010) Positional communication and private information in honeybee foraging models. In Swarm intelligence (pp. 263–274). Berlin: Springer.Google Scholar
  2. Bartumeus, F., da Luz, M. G. E., Viswanathan, G. M., & Catalan, J. (2005). Animal search strategies: A quantitative random-walk analysis. Ecology, 86(11), 3078–3087.CrossRefGoogle Scholar
  3. 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: Biological Sciences, 263(1376), 1565–1569.CrossRefGoogle Scholar
  4. Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., & Mondada, F. (2010) The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4187–4193). IEEE Press.Google Scholar
  5. Borenstein, J., & Koren, Y. (1989). Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 1179–1187.CrossRefGoogle Scholar
  6. Castello, E., Yamamoto, T., Libera, F. D., Liu, W., Winfield, A. F. T., Nakamura, Y., et al. (2015). Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach. Swarm Intelligence, 10(1), 1–31.CrossRefGoogle Scholar
  7. Cheein, F. A. A., & Carelli, R. (2013). Agricultural robotics: Unmanned robotic service units in agricultural tasks. IEEE Industrial Electronics Magazine, 7(3), 48–58.CrossRefGoogle Scholar
  8. Dimidov, C., Oriolo, G., & Trianni, V. (2016) Random walks in swarm robotics: An experiment with kilobots. In M. Dorigo, M. Birattari, X. Li, M. López-Ibáñez, K. Ohkura, C. Pinciroli, & T. Stützle (Eds.), Proceedings of the 10th international conference on swarm intelligence (ANTS 2016), volume 9882 of LNCS (pp. 185–196). New York: Springer.Google Scholar
  9. Dorigo, M., Floreano, D., Gambardella, L., 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.CrossRefGoogle Scholar
  10. Dornhaus, A., Klügl, F., Oechslein, C., Puppe, F., & Chittka, L. (2006). Benefits of recruitment in honey bees: Effects of ecology and colony size in an individual-based model. Behavioral Ecology, 17(3), 336–344.CrossRefGoogle Scholar
  11. Ducatelle, F., Di Caro, G. A., Forster, A., Bonani, M., Dorigo, M., Magnenat, S., et al. (2014). Cooperative navigation in robotic swarms. Swarm Intelligence, 8(1), 1–33.CrossRefGoogle Scholar
  12. Granovskiy, B., Latty, T., Duncan, M., Sumpter, D. J. T., & Beekman, M. (2012). How dancing honey bees keep track of changes: The role of inspector bees. Behavioral Ecology, 23(3), 588–596.CrossRefGoogle Scholar
  13. Gutiérrez, A., Campo, A., Monasterio-Huelin, F., Magdalena, L., & Dorigo, M. (2010). Collective decision-making based on social odometry. Neural Computing & Applications, 19(6), 807–823.CrossRefGoogle Scholar
  14. Hecker, J. P., & Moses, M. E. (2015). Beyond pheromones: Evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intelligence, 9(1), 1–28.CrossRefGoogle Scholar
  15. Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.CrossRefGoogle Scholar
  16. Hui, C. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192(1–2), 317–320.CrossRefGoogle Scholar
  17. Krieger, M. J. B., Billeter, J.-B., & Keller, L. (2000). Ant-like task allocation and recruitment in cooperative robots. Nature, 406(6799), 992–995.CrossRefGoogle Scholar
  18. 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 Adaptive Systems, 1(1), 4–25.CrossRefGoogle Scholar
  19. Liemhetcharat, S., Yan, R., & Tee, K. P. (2015). Continuous foraging and information gathering in a multi-agent team. In Proceedings of the 2015 international conference on autonomous agents and multiagent systems (AAMAS) (pp. 1325–1333). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
  20. Liu, W., & Winfield, A. F. T. (2010). Modeling and optimization of adaptive foraging in swarm robotic systems. The International Journal of Robotics Research, 29(14), 1743–1760.CrossRefGoogle Scholar
  21. Liu, W., Winfield, A. F. T., Sa, J., Chen, J., & Dou, L. (2007). Towards energy optimization: Emergent task allocation in a swarm of foraging robots. Adaptive Behavior, 15(3), 289–305.CrossRefGoogle Scholar
  22. Loreto, V., Baronchelli, A., Mukherjee, A., Puglisi, A., & Tria, F. (2011). Statistical physics of language dynamics. Journal of Statistical Mechanics: Theory and Experiment, 2011(04), P04006.CrossRefGoogle Scholar
  23. Miletitch, R., Trianni, V., Campo, A., & Dorigo, M. (2013) Information aggregation mechanisms in social odometry. In Proceedings of the 20th European conference on artificial life (ECAL 2013) (pp. 102–109). Cambridge, MA: MIT Press.Google Scholar
  24. Moretti, P., Baronchelli, A., Starnini, M., & Pastor-Satorras, R. (2013). Generalized voter-like models on heterogeneous networks. In A. Mukherjee, M. Choudhury, F. Peruani, N. Ganguly, & B. Mitra (Eds.), Dynamics on and of complex networks, volume 2: Applications to time-varying dynamical systems (pp. 285–300). New York: Springer.CrossRefGoogle Scholar
  25. Murphy, R. R., Tadokoro, S., Nardi, D., Jacoff, A., Fiorini, P., Choset, H., & Erkmen, A. M. (2008). Search and rescue robotics. In Springer handbook of robotics (pp. 1151–1173). Springer.Google Scholar
  26. Pais, D., Hogan, P. M., Schlegel, T., Franks, N. R., Leonard, N. E., & Marshall, J. A. R. (2013). A mechanism for value-sensitive decision-making. PLoS ONE, 8(9), e73216.CrossRefGoogle Scholar
  27. Perna, A., & Latty, T. (2014). Animal transportation networks. Journal of The Royal Society Interface, 11(100), 20140334–20140334.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. Pitonakova, L., Crowder, R., & Bullock, S. (2016). Information flow principles for plasticity in foraging robot swarms. Swarm Intelligence, 10(1), 33–63.CrossRefGoogle Scholar
  30. Reina, A., Marshall, J. A. R., Trianni, V., & Bose, T. (2017). Model of the best-of-n nest-site selection process in honeybees. Physical Review E, 95(5), 052411–15.CrossRefGoogle Scholar
  31. Reina, A., Miletitch, R., Dorigo, M., & Trianni, V. (2015a). A quantitative micro-macro link for collective decisions: the shortest path discovery/selection example. Swarm Intelligence, 9(2–3), 75–102.CrossRefGoogle Scholar
  32. Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., & Trianni, V. (2015b). A design pattern for decentralised decision making. PLoS ONE, 10(10), e0140950–18.CrossRefGoogle Scholar
  33. Roberts, J., Stirling, T. S., Zufferey, J.-C., & Floreano, D. (2009) 2.5D infrared range and bearing system for collective robotics. In Proceedings of the 2009 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3659–3664). IEEE Press.Google Scholar
  34. Saleh, N., & Chittka, L. (2006). Traplining in bumblebees (Bombus impatiens): A foraging strategy’s ontogeny and the importance of spatial reference memory in short-range foraging. Oecologia, 151(4), 719–730.CrossRefGoogle Scholar
  35. Schroeder, A., Ramakrishnan, S., Kumar, M., & Trease, B. (2017). Efficient spatial coverage by a robot swarm based on an ant foraging model and the lévy distribution. Swarm Intelligence, 11(1), 39–69.CrossRefGoogle Scholar
  36. Seeley, T. D., Visscher, P. K., Schlegel, T., Hogan, P. M., Franks, N. R., & Marshall, J. A. R. (2012). Stop signals provide cross inhibition in collective decision-making by Honeybee swarms. Science, 335(6064), 108–111.CrossRefGoogle Scholar
  37. Simpson, S. J., Sibly, R. M., Lee, K. P., Behmer, S. T., & Raubenheimer, D. (2004). Optimal foraging when regulating intake of multiple nutrients. Animal Behaviour, 68(6), 1299–1311.CrossRefGoogle Scholar
  38. Song, Z., & Vaughan, R. T. (2013) Sustainable robot foraging: Adaptive fine-grained multi-robot task allocation for maximum sustainable yield of biological resources. In Proceedings of the 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3309–3316). IEEE Press.Google Scholar
  39. Spranger, M. (2013). Evolving grounded spatial language strategies. Künstliche Intelligenz, 27(2), 97–106.CrossRefGoogle Scholar
  40. Steels, L., & Belpaeme, T. (2005). Coordinating perceptually grounded categories through language: A case study for colour. The Behavioral and brain sciences, 28(04), 1–61.Google Scholar
  41. Trianni, V., & Campo, A. (2015). Fundamental collective behaviors in swarm robotics. In J. Kacprzyk & W. Pedrycz (Eds.), Springer handbook of computational intelligence (pp. 1377–1394). Berlin: Springer.CrossRefGoogle Scholar
  42. Trianni, V., & Dorigo, M. (2005). Emergent collective decisions in a swarm of robots. In Proceedings of the 2005 IEEE swarm intelligence symposium (SIS 2005) (pp. 241–248).Google Scholar
  43. Valentini, G., Ferrante, E., & Dorigo, M. (2017). The best-of-n problem in robot swarms: Formalization, state of the art, and novel perspectives. Frontiers in Robotics and AI, 4, 1–43.CrossRefGoogle Scholar
  44. Winfield, A. F. (2009). Foraging robots. In Encyclopedia of complexity and systems science (pp. 3682–3700). New York: Springer.CrossRefGoogle Scholar
  45. Yoshida, K. (2009). Achievements in space robotics. IEEE Robotics & Automation Magazine, 16(4), 20–28.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium
  2. 2.ISTCItalian National Research CouncilRomeItaly

Personalised recommendations