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Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails

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Swarm Intelligence (ANTS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

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Abstract

Large swarms of simple autonomous robots can be employed to find objects clustered at random locations, and transport them to a central depot. This solution offers system parallelisation through concurrent environment exploration and object collection by several robots, but it also introduces the challenge of robot coordination. Inspired by ants’ foraging behaviour, we successfully tackle robot swarm coordination through indirect stigmergic communication in the form of virtual pheromone trails. We design and implement a robot swarm composed of up to 100 Kilobots using the recent technology Augmented Reality for Kilobots (ARK). Using pheromone trails, our memoryless robots rediscover object sources that have been located previously. The emerging collective dynamics show a throughput inversely proportional to the source distance. We assume environments with multiple sources, each providing objects of different qualities, and we investigate how the robot swarm balances the quality-distance trade-off by using quality-sensitive pheromone trails. To our knowledge this work represents the largest robotic experiment in stigmergic foraging, and is the first complete demonstration of ARK, showcasing the set of unique functionalities it provides.

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References

  1. Arvin, F., Yue, S., Xiong, C.: Colias-\(\phi \): an autonomous micro robot for artificial pheromone communication. Int. J. Mech. Eng. Robot. Res. 4(4), 349–353 (2015)

    Google Scholar 

  2. Berman, S., Kumar, V., Nagpal, R.: Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. In: Proceedings of the IEEE/RSJ International Conference on Robotics and Automation, ICRA 2011, pp. 378–385. IEEE Press (2011)

    Google Scholar 

  3. Bosien, A., Turau, V., Zambonelli, F.: Approaches to fast sequential inventory and path following in RFID-enriched environments. Int. J. Radio Freq. Identif. Technol. Appl. 4(1), 28 (2012)

    Google Scholar 

  4. Campo, A., Dorigo, M.: Efficient multi-foraging in swarm robotics. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS, vol. 4648, pp. 696–705. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74913-4_70

    Chapter  Google Scholar 

  5. Campo, A., et al.: Artificial pheromone for path selection by a foraging swarm of robots. Biol. Cybern. 103(5), 339–352 (2010)

    Article  Google Scholar 

  6. Couzin, I.D., Krause, J., James, R., Ruxton, G.D., Franks, N.R.: Collective memory and spatial sorting in animal groups. J. Theoret. Biol. 218(1), 1–11 (2002)

    Article  MathSciNet  Google Scholar 

  7. Detrain, C., Deneubourg, J.L.: Self-organized structures in a superorganism: do ants “behave” like molecules? Phys. Life Rev. 3(3), 162–187 (2006)

    Article  Google Scholar 

  8. Detrain, C., Deneubourg, J.L.: Collective decision-making and foraging patterns in ants and honeybees. Adv. Insect Physiol. 35(08), 123–173 (2008)

    Article  Google Scholar 

  9. Dimidov, C., Oriolo, G., Trianni, V.: Random walks in swarm robotics: an experiment with Kilobots. In: Dorigo, M., et al. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 185–196. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44427-7_16

    Chapter  Google Scholar 

  10. Ducatelle, F., Di Caro, G.A., Pinciroli, C., Gambardella, L.M.: Self-organized cooperation between robotic swarms. Swarm Intell. 5(2), 73–96 (2011)

    Article  Google Scholar 

  11. Ducatelle, F., Di Caro, G.A., Pinciroli, C., Mondada, F., Gambardella, L.M.: Communication assisted navigation in robotic swarms: self-organization and cooperation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, pp. 4981–4988. IEEE Press (2011)

    Google Scholar 

  12. Dussutour, A., Nicolis, S.C., Shephard, G., Beekman, M., Sumpter, D.J.T.: The role of multiple pheromones in food recruitment by ants. J. Exp. Biol. 212(15), 2337–2348 (2009)

    Article  Google Scholar 

  13. Ferrante, E., Turgut, A.E., Duéñez-Guzmán, E., Dorigo, M., Wenseleers, T.: Evolution of self-organized task specialization in robot swarms. PLoS Comput. Biol. 11(8), 1–21 (2015)

    Article  Google Scholar 

  14. Flanagan, T.P., Letendre, K., Burnside, W.R., Fricke, G.M., Moses, M.E.: Quantifying the effect of colony size and food distribution on harvester ant foraging. PLoS One 7(7), e39427 (2012)

    Article  Google Scholar 

  15. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., Matsuno, F.: Dependency by concentration of pheromone trail for multiple robots. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 283–290. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87527-7_28

    Chapter  Google Scholar 

  16. Fujisawa, R., Dobata, S., Sugawara, K., Matsuno, F.: Designing pheromone communication in swarm robotics: group foraging behavior mediated by chemical substance. Swarm Intell. 8(3), 227–246 (2014)

    Article  Google Scholar 

  17. Garnier, S., Combe, M., Jost, C., Theraulaz, G.: Do ants need to estimate the geometrical properties of trail bifurcations to find an efficient route? A swarm robotics test bed. PLoS Comput. Biol. 9(3), e1002903 (2013)

    Article  MathSciNet  Google Scholar 

  18. Garnier, S., Tâche, F., Combe, M., Grimal, A., Theraulaz, G.: Alice in pheromone land: an experimental setup for the study of ant-like robots. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007, pp. 37–44. IEEE Press (2007)

    Google Scholar 

  19. Goss, S., Deneubourg, J.L., Bourgine, P., Varela, E.: Harvesting by a group of robots. In: 1st European Conference on Artificial Life, pp. 195–204. MIT Press, Cambridge (1992)

    Google Scholar 

  20. Hamann, H., Wörn, H.: An analytical and spatial model of foraging in a swarm of robots. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SR 2006. LNCS, vol. 4433, pp. 43–55. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71541-2_4

    Chapter  Google Scholar 

  21. Hecker, J.P., Letendre, K., Stolleis, K., Washington, D., Moses, M.E.: Formica ex Machina: ant swarm foraging from physical to virtual and back again. In: Dorigo, M., et al. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 252–259. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32650-9_25

    Chapter  Google Scholar 

  22. Heredia, A., Detrain, C.: Influence of seed size and seed nature on recruitment in the polymorphic harvester ant Messor barbarus. Behav. Process. 70(3), 289–300 (2005)

    Article  Google Scholar 

  23. Herianto, Kurabayashi, D.: Realization of an artificial pheromone system in random data carriers using RFID tags for autonomous navigation. In: Proceedings of the IEEE/RSJ International Conference on Robotics and Automation, ICRA 2009, pp. 2288–2293. IEEE Press (2009)

    Google Scholar 

  24. Herianto, Sakakibara, T., Kurabayashi, D.: Artificial pheromone system using RFID for navigation of autonomous robots. J. Bion. Eng. 4(4), 245–253 (2007)

    Article  Google Scholar 

  25. Hoff, N., Wood, R., Nagpal, R.: Distributed colony-level algorithm switching for robot swarm foraging. In: Martinoli, A. (ed.) Distributed Autonomous Robotic Systems. STAR, vol. 83, pp. 417–430. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  26. Hölldobler, B., Wilson, E.O.: The Ants. Harvard University Press, Cambridge (1990)

    Book  Google Scholar 

  27. Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: Morán, F., Moreno, A., Merelo, J.J., Chacón, P. (eds.) ECAL 1995. LNCS, vol. 929, pp. 704–720. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59496-5_337

    Chapter  Google Scholar 

  28. Jeanson, R., Deneubourg, J.L., Grimal, A., Theraulaz, G.: Modulation of individual behavior and collective decision-making during aggregation site selection by the ant Messor barbarus. Behav. Ecol. Sociobiol. 55(4), 388–394 (2004)

    Article  Google Scholar 

  29. Khaliq, A.A., Di Rocco, M., Saffiotti, A.: Stigmergic algorithms for multiple minimalistic robots on an RFID floor. Swarm Intell. 8(3), 199–225 (2014)

    Article  Google Scholar 

  30. Macarthur, R.H., Pianka, E.R.: On optimal use of a patchy environment. Am. Nat. 100(916), 603–609 (1966)

    Article  Google Scholar 

  31. Mailleux, A.C., Deneubourg, J.L., Detrain, C.: Regulation of ants’ foraging to resource productivity. Proc. Roy. Soc. Lond. B: Biol. Sci. 270(1524), 1609–1616 (2003)

    Article  Google Scholar 

  32. Mamei, M., Zambonelli, F.: Physical deployment of digital pheromones through RFID technology. In: Proceedings of the 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 281–288. IEEE Press (2005)

    Google Scholar 

  33. Mamei, M., Zambonelli, F.: Pervasive pheromone-based interaction with RFID tags. ACM Trans. Auton. Adapt. Syst. 2(2), 4 (2007)

    Article  Google Scholar 

  34. Mayet, R., Roberz, J., Schmickl, T., Crailsheim, K.: Antbots: a feasible visual emulation of pheromone trails for swarm robots. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 84–94. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15461-4_8

    Chapter  Google Scholar 

  35. Nicolis, S.C., Deneubourg, J.L.: Emerging patterns and food recruitment in ants: an analytical study. J. Theoret. Biol. 198(4), 575–592 (1999)

    Article  Google Scholar 

  36. Nouyan, S., Groß, R., Bonani, M., Mondada, F., Dorigo, M.: Teamwork in self-organized robot colonies. IEEE Trans. Evol. Comput. 13(4), 695–711 (2009)

    Article  Google Scholar 

  37. Olsson, O., Brown, J.S., Helf, K.L.: A guide to central place effects in foraging. Theoret. Popul. Biol. 74(1), 22–33 (2008)

    Article  Google Scholar 

  38. Orians, G.H., Pearson, N.E.: On the theory of central place foraging. Anal. Ecol. Syst. 154–177 (1979)

    Google Scholar 

  39. Payton, D.W., Daily, M., Estowski, R., Howard, M., Lee, C.: Pheromone robotics. Auton. Robots 11(3), 319–324 (2001)

    Article  Google Scholar 

  40. Pinciroli, C., Talamali, M.S., Reina, A., Marshall, J.A.R., Trianni, V.: Simulating Kilobots within ARGoS: models and experimental validation. In: Dorigo, M., et al. (eds.) ANTS 2018. Lecture Notes in Computer Science, vol. 11172, pp. 176–187. Springer, Heidelberg (2018)

    Google Scholar 

  41. Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)

    Article  Google Scholar 

  42. Pini, G., Brutschy, A., Scheidler, A., Dorigo, M., Birattari, M.: Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer. Artif. Life 20(3), 291–317 (2014)

    Article  Google Scholar 

  43. Pitonakova, L., Crowder, R., Bullock, S.: Information flow principles for plasticity in foraging robot swarms. Swarm Intell. 10(1), 33–63 (2016)

    Article  Google Scholar 

  44. Pitonakova, L., Crowder, R., Bullock, S.: The Information-Cost-Reward framework for understanding robot swarm foraging. Swarm Intell. 12(1), 71–96 (2018)

    Article  Google Scholar 

  45. Purnamadjaja, A.H., Russell, R.A.: Guiding robots’ behaviors using pheromone communication. Auton. Robots 23(2), 113–130 (2007)

    Article  Google Scholar 

  46. Pyke, G.H.: Optimal foraging theory: a critical review. Annu. Rev. Ecol. Evol. Syst. 15, 523–75 (1984)

    Article  Google Scholar 

  47. Reina, A., Cope, A.J., Nikolaidis, E., Marshall, J.A.R., Sabo, C.: ARK: augmented reality for Kilobots. IEEE Robot. Autom. Lett. 2(3), 1755–1761 (2017)

    Article  Google Scholar 

  48. Reina, A., Miletitch, R., Dorigo, M., Trianni, V.: A quantitative micro-macro link for collective decisions: the shortest path discovery/selection example. Swarm Intell. 9(2–3), 75–102 (2015)

    Article  Google Scholar 

  49. Robinson, E.J., Ratnieks, F.L., Holcombe, M.: An agent-based model to investigate the roles of attractive and repellent pheromones in ant decision making during foraging. J. Theoret. Biol. 255(2), 250–258 (2008)

    Article  MathSciNet  Google Scholar 

  50. Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., Nagpal, R.: Kilobot: a low cost robot with scalable operations designed for collective behaviors. Robot. Auton. Syst. 62(7), 966–975 (2014)

    Article  Google Scholar 

  51. Scheidler, A., Brutschy, A., Ferrante, E., Dorigo, M.: The k-unanimity rule for self-organized decision-making in swarms of robots. IEEE Trans. Cybern. 46(5), 1175–1188 (2016)

    Article  Google Scholar 

  52. Seeley, T.D.: Honey bee foragers as sensory units of their colonies. Behav. Ecol. Sociobiol. 34(1), 51–62 (1994)

    Article  Google Scholar 

  53. Sperati, V., Trianni, V., Nolfi, S.: Self-organised path formation in a swarm of robots. Swarm Intell. 5(2), 97–119 (2011)

    Article  Google Scholar 

  54. Sugawara, K., Kazama, T., Watanabe, T.: Foraging behavior of interacting robots with virtual pheromone. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004, vol. 3, pp. 3074–3079. IEEE Press (2004)

    Google Scholar 

  55. Svennebring, J., Koenig, S.: Building terrain-covering ant robots: a feasibility study. Auton. Robots 16(3), 313–332 (2004)

    Article  Google Scholar 

  56. Theraulaz, G., Bonabeau, E.: A brief history of stigmergy. Artif. Life 5(2), 97–116 (1999)

    Article  Google Scholar 

  57. Ulam, P., Balch, T.: Using optimal foraging models to evaluate learned robotic foraging behavior. Adapt. Behav. 12(3–4), 213–222 (2004)

    Article  Google Scholar 

  58. Valentini, G., et al.: Kilogrid: a novel experimental environment for the kilobot robot. Swarm Intell. 1–22 (2018)

    Google Scholar 

  59. Werger, B.B., Matarić, M.J.: Robotic “food” chains: externalization of state and program for minimal-agent foraging. In: From Animals to Animats 4. Proceedings of the 4th International Conference on Simulation of Adaptive Behavior, SAB 1996, pp. 625–634. MIT Press, Cambridge (1996)

    Google Scholar 

  60. Wilson, E.O.: Chemical communication among workers of the fire ant Solenopsis saevissima (Fr. Smith): the organization of mass-foraging. Anim. Behav. 10(1–2), 134–147 (1962)

    Article  Google Scholar 

  61. Winfield, A.F.T.: Foraging robots. In: Encyclopedia of Complexity and System Science, pp. 3682–3700. Springer, Heidelberg (2009). https://doi.org/10.1007/978-0-387-30440-3

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Acknowledgments

This work was funded by the ERC under the EU-H2020 research and innovation programme (grant agreement 647704). The authors thank Michael Port, Alex Cope, and Carlo Pinciroli for their crucial help and support in tackling the hardware and software challenges of this project.

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Authors and Affiliations

  1. Department of Computer Science, University of Sheffield, Sheffield, UK

    Anna Font Llenas, Mohamed S. Talamali, James A. R. Marshall & Andreagiovanni Reina

  2. Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK

    Anna Font Llenas & Xu Xu

  3. MERI, Sheffield Hallam University, Sheffield, UK

    Xu Xu

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  1. Anna Font Llenas
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  2. Mohamed S. Talamali
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  3. Xu Xu
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  4. James A. R. Marshall
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  5. Andreagiovanni Reina
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Correspondence to Andreagiovanni Reina .

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Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Font Llenas, A., Talamali, M.S., Xu, X., Marshall, J.A.R., Reina, A. (2018). Quality-Sensitive Foraging by a Robot Swarm Through Virtual Pheromone Trails. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_11

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