Recruitment-Based Robotic Colony Allocation

  • Chloe FlemingEmail author
  • Julie A. Adams
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 9)


Robotic colonies for distributed field operations require intelligent algorithms for allocating assets based on time-varying task requirements. Agricultural applications, including crop pollination, involve dynamic schedules that are unknown a priori; thus, an effective colony must characterize task completion and respond to perceived changes. Insect colonies rely on individuals’ observations to allocate their workforces to tasks, such as foraging in the richest flower patches. Biologically-inspired strategies for gathering observations while providing field coverage were formulated and integrated into a colony recruitment model. The strategies’ coverage results and efficiency were evaluated with varying task schedules, colony placements, and multiple colonies, yielding 50–100% pollination across all independent variables. Splitting a single colony into multiple small colonies improved coverage and efficiency.


Swarm intelligence Honeybees Collective decision-making 



This research was supported by a Graduate Teaching Assistantship from the College of Engineering at Oregon State University.


  1. 1.
    Becher, M.A., Grimm, V., Knapp, J., Horn, J., Twiston-Davies, G., Osborne, J.L.: BEESCOUT: a model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE. Ecol. Model. 340, 126–133 (2016)Google Scholar
  2. 2.
    Beekman, M., Ratnieks, F.L.W.: Long-range foraging by the honey-bee, Apis mellifera L. Funct. Ecol. 14(4), 490–496 (1998). isi:000089054800011Google Scholar
  3. 3.
    Berman, S., Halász, Á., Hsieh, M.A., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Trans. Robot. 25(4), 927–937 (2009)Google Scholar
  4. 4.
    Berman, S., Nagpal, R., Halász, Á.: Optimization of stochastic strategies for spatially inhomogeneous robot swarms: a case study in commercial pollination. In: IEEE International Conference on Intelligent Robots and Systems (2011)Google Scholar
  5. 5.
    Bodi, M., Thenius, R., Szopek, M., Schmickl, T., Crailsheim, K.: Interaction of robot swarms using the honeybee-inspired control algorithm BEECLUST. Math. Comput. Modell. Dyn. Syst. 18(1), 87–100 (2012)zbMATHGoogle Scholar
  6. 6.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)Google Scholar
  7. 7.
    Camazine, S.: The regulation of pollen foraging by honey bees: how foragers assess the colony’s need for pollen. Behav. Ecol. Sociobiol. 32(4), 265–272 (1993)Google Scholar
  8. 8.
    Cody, J.R., Adams, J.A.: An evaluation of quorum sensing mechanisms in collective value-sensitive site selection. In: IEEE International Symposium on Multi-robot and Multi-agent Systems, pp. 40–47 (2017)Google Scholar
  9. 9.
    Cunningham, S.A., Fournier, A., Neave, M.J., Le Feuvre, D.: Improving spatial arrangement of honeybee colonies to avoid pollination shortfall and depressed fruit set. J. Appl. Ecol. 53(2), 350–359 (2016)Google Scholar
  10. 10.
    Díaz, P.C., Arenas, A., Fernández, V.M., Susic Martin, C., Basilio, A.M., Farina, W.M.: Honeybee cognitive ecology in a fluctuating agricultural setting of apple and pear trees. Behav. Ecol. 24(5), 1058–1067 (2013)Google Scholar
  11. 11.
    Dornhaus, A., Klügl, F., Oechslein, C., Puppe, F., Chittka, L.: Benefits of recruitment in honey bees: effects of ecology and colony size in an individual-based model. Behav. Ecol. 17(3), 336–344 (2006)Google Scholar
  12. 12.
    Goulson, D.: Why do pollinators visit proportionally fewer flowers in large patches? Oikos 91(3), 485–492 (2000)Google Scholar
  13. 13.
    Greggers, U., Menzel, R.: Memory dynamics and foraging strategies of honeybees. Behav. Ecol. Sociobiol. 32(1), 17–29 (1993)Google Scholar
  14. 14.
    Gyan, K.Y., Woodell, S.R.J.: Analysis of insect pollen loads and pollination efficiency of some common insect visitors of four species of woody Rosaceae. Funct. Ecol. 1(3), 269–274 (1987). Scholar
  15. 15.
    Hecker, J.P., Moses, M.E.: Beyond pheromones: evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intell. 9(1), 43–70 (2015)Google Scholar
  16. 16.
    Klatt, B.K., Holzschuh, A., Westphal, C., Clough, Y., Smit, I., Pawelzik, E., Tscharntke, T.: Bee pollination improves crop quality, shelf life and commercial value. Proc. R. Soc. B: Biol. Sci. 281(1775) (2013). Scholar
  17. 17.
    Krieger, M.J.B., Billeter, J.B., Keller, L.: Ant-like task allocation and recruitment in cooperative robots. Nature 406(6799), 992–995 (2000)Google Scholar
  18. 18.
    Pitonakova, L., Crowder, R., Bullock, S.: Information flow principles for plasticity in foraging robot swarms. Swarm Intell. 10(1), 33–63 (2016)Google Scholar
  19. 19.
    Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., Trianni, V.: A design pattern for decentralised decision making. PLoS ONE 10(10) (2015)Google Scholar
  20. 20.
    Riley, J.R., Greggers, U., Smith, A.D., Reynolds, D.R., Menzel, R.: The flight paths of honeybees recruited by the waggle dance. Nature 435(7039), 205 (2005)Google Scholar
  21. 21.
    Rucker, R.R., Thurman, W.N., Burgett, M.: Honey bee pollination markets and the internalization of reciprocal benefits. Am. J. Agric. Econ. 94(4), 956–977 (2012)Google Scholar
  22. 22.
    Sampson, B.J., Cane, J.H.: Pollination efficiencies of three bee (hymenoptera: Apoidea) species visiting rabbiteye blueberry. J. Econ. Entomol. 93(6), 1726–1731 (2000)Google Scholar
  23. 23.
    Seeley, T.D.: Division of labor between scouts and recruits in honeybee foraging. Behav. Ecol. Sociobiol. 12(3), 253–259 (1983)Google Scholar
  24. 24.
    Seeley, T.D.: Social foraging by honeybees: how colonies allocate foragers among patches of flowers. Behav. Ecol. Sociobiol. 19(5), 343–354 (1986)Google Scholar
  25. 25.
    Seeley, T.D., Mikheyev, A.S., Pagano, G.J.: Dancing bees tune both duration and rate of waggle-run production in relation to nectar-source profitability. J. Compar. Physiol. A 186(9), 813–819 (2000)Google Scholar
  26. 26.
    Seeley, T.D., Visscher, P.K., Schlegel, T., Hogan, P.M., Franks, N.R., Marshall, J.A.R.: Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335(6064), 108–111 (2012)Google Scholar
  27. 27.
    Sumpter, D.J.T.: Collective Animal Behavior. Princeton University Press (2010)Google Scholar
  28. 28.
    Thenius, R., Schmickl, T., Crailsheim, K.: Optimisation of a honeybee-colony’s energetics via social learning based on queuing delays. Connect. Sci. 20(2–3), 193–210 (2008)Google Scholar
  29. 29.
    Tonutti, P., Bargioni, G., Cossio, F., Ramina, A.: Effective pollination period and ovule longevity in prunus avium l. Adv. Hortic. Sci. 5(4), 1000–1006 (1991)Google Scholar
  30. 30.
    Valentini, G., Hamann, H., Dorigo, M.: Global-to-local design for self-organized task allocation in swarms. Technical report. IRIDIA, Universite Libre de Bruxelles, Bruxelles, Belgium (2016)Google Scholar
  31. 31.
    vanEngelsdorp, D., Evans, J.D., Saegerman, C., Mullin, C., Haubruge, E., et al.: Colony collapse disorder: a descriptive study. PLoS ONE 4(8) (2009)Google Scholar
  32. 32.
    Von Frisch, K.: The Dance Language and Orientation of Bees (1967)Google Scholar
  33. 33.
    de Vries, H., Biesmeijer, J.C.: Modelling collective foraging by means of individual behaviour rules in honey-bees. Behav. Ecol. Sociobiol. 44(2), 109–124 (1998)Google Scholar
  34. 34.
    Wedde, H.F., Farooq, M., Pannenbaecker, T., Vogel, B., Mueller, C., Meth, J., Jeruschkat, R.: BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 153–160. ACM(2005)Google Scholar
  35. 35.
    Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 83–94. Springer (2004)Google Scholar
  36. 36.
    Wenner, A.: The flight speed of honeybees: a quantitative approach. J. Apic. Res. 2(1), 25–32 (1963)Google Scholar
  37. 37.
    Whiting, M.D., Salazar, M.R., Hoogenboom, G.: Development of bloom phenology models for tree fruits. In: IX International Symposium on Modelling in Fruit Research and Orchard Management, vol. 1068, pp. 107–112 (2011)Google Scholar
  38. 38.
    Winfree, R.: Pollinator-dependent crops: an increasingly risky business. Curr. Biol. 18(20), 968–969 (2008). Scholar

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Authors and Affiliations

  1. 1.Collaborative Robotics & Intelligent Systems InstituteOregon State UniversityCorvallisUSA

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