Skip to main content
SpringerLink
Log in

Investigation of Cue-Based Aggregation Behaviour in Complex Environments

  • Conference paper
  • First Online:
Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

Abstract

Swarm robotics is mainly inspired by the collective behaviour of social animals in nature. Among different behaviours such as foraging and flocking performed by social animals; aggregation behaviour is often considered as the most basic and fundamental one. Aggregation behaviour has been studied in different domains for over a decade. In most of these studies, the settings are over-simplified that are quite far from reality. In this paper, we investigate cue-based aggregation behaviour using BEECLUST in a complex environment having two cues –one being the local optimum and the other being the global optimum– with an obstacle between the two cues. The robotic validation of the BEECLUST strategy in a complex environment is the main motivation of this paper. We measured the aggregation size on both cues with and without the obstacle varying the number of robots. The simulations were performed on a custom open-source simulation platform, Bee-Ground, using MONA robots. The results showed that the aggregation behaviour with BEECLUST strategy was able to overcome a certain degree of environmental complexities revealing the robustness of the method. We also verified these results using our stock-flow model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bee-Ground: Open-sourced simulation tool for aggregation of robotics swarms. https://github.com/wshiyi-cn/BeeGround. Accessed 15 Jan 2020

  2. Vensim\(^{TM}\). http://www.vensim.com. Accessed 15 Jan 2020

  3. Arvin, F., Espinosa, J., Bird, B., West, A., Watson, S., Lennox, B.: Mona: an affordable open-source mobile robot for education and research. J. Intell. Robot. Syst. 94(3–4), 761–775 (2019)

    Article  Google Scholar 

  4. Arvin, F., Murray, J., Zhang, C., Yue, S.: Colias: an autonomous micro robot for swarm robotic applications. Int. J. Adv. Robot. Syst. 11, 1 (2014)

    Article  Google Scholar 

  5. Arvin, F., Turgut, A.E., Bellotto, N., Yue, S.: Comparison of different cue-based swarm aggregation strategies. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) ICSI 2014. LNCS, vol. 8794, pp. 1–8. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11857-4_1

    Chapter  Google Scholar 

  6. Arvin, F., et al.: \(\Phi \)Clust: pheromone-based aggregation for robotic swarms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4288–4294 (2018)

    Google Scholar 

  7. Arvin, F., Turgut, A.E., Krajník, T., Yue, S.: Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adapt. Behav. 24(2), 102–118 (2016)

    Article  Google Scholar 

  8. Arvin, F., Turgut, A.E., Krajník, T., Yue, S.: Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adapt. Behav. 24(2), 102–118 (2016). https://doi.org/10.1177/1059712316632851

    Article  Google Scholar 

  9. Bodi, M., Thenius, R., Szopek, M., Schmickl, T., Crailsheim, K.: Interaction of robot swarms using the honeybee-inspired control algorithm beeclust. Math. Comput. Model. Dyn. Syst. 18(1), 87–100 (2012)

    Article  Google Scholar 

  10. Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7, 1–41 (2013). https://doi.org/10.1007/sd11721-012-0075-2

    Article  Google Scholar 

  11. Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Bonabeau, E., Theraula, G.: Self-organization in Biological Systems. Princeton University Press, Princeton (2003)

    MATH  Google Scholar 

  12. Caverzasi, E., Godin, A.: Stock-flow consistent modeling through the ages. Levy Economics Institute of Bard College Working Paper, no. 745 (2013)

    Google Scholar 

  13. Correll, N., Martinoli, A.: Modeling self-organized aggregation in a swarm of miniature robots. In: IEEE International Conference on Robotics and Automation: Workshop on Collective Behaviors inspired by Biological and Biochemical Systems (2007)

    Google Scholar 

  14. Frank, D., Jouandet, G., Kearney, P., Macpherson, L., Gallio, M.: Temperature representation in the drosophila brain. Nature 519, 358–361 (2015). https://doi.org/10.1038/nature14284

    Article  Google Scholar 

  15. Gauci, M., Chen, J., Li, W., Dodd, T.J., Groß, R.: Self-organized aggregation without computation. Int. J. Robot. Res. 33(8), 1145–1161 (2014)

    Article  Google Scholar 

  16. Grünbaum, D., Okubo, A.: Modeling social animal aggregations. In: Levin, S.A. (ed.) Frontiers in Mathematical Biology. Lecture Notes in Biomathematics, vol. 100, pp. 296–325. Springer, Heidelberg (1994). https://doi.org/10.1007/978-3-642-50124-1_18

    Chapter  Google Scholar 

  17. Heran, H.: Untersuchungen über den Temperatursinn der Honigbiene (Apis mellifica) unter besonderer Berücksichtigung der Wahrnehmung strahlender Wärme. J. Comparat. Physiol. 34, 179–206 (1952). https://doi.org/10.1007/BF00339537

    Article  Google Scholar 

  18. Holland, O., Melhuish, C.: An interactive method for controlling group size in multiple mobile robot systems. In: 8th International Conference on Advanced Robotics, pp. 201–206 (1997)

    Google Scholar 

  19. Hu, C., Arvin, F., Xiong, C., Yue, S.: Bio-inspired embedded vision system for autonomous micro-robots: the LGMD case. IEEE Trans. Cogn. Dev. Syst. 9(3), 241–254 (2016)

    Article  Google Scholar 

  20. Krajník, T., et al.: A practical multirobot localization system. J. Intell. Robot. Syst. 76(3–4), 539–562 (2014)

    Article  Google Scholar 

  21. Krestovnikov, K., Cherskikh, E., Ronzhin, A.: Mathematical model of a swarm robotic system with wireless bi-directional energy transfer. In: Kravets, A.G. (ed.) Robotics: Industry 4.0 Issues & New Intelligent Control Paradigms. SSDC, vol. 272, pp. 13–23. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37841-7_2

    Chapter  Google Scholar 

  22. Kube, C.R., Zhang, H.: Collective robotics: from social insects to robots. Adapt. Behav. 2(2), 189–218 (1993)

    Article  Google Scholar 

  23. Mermoud, G., Matthey, L., Evans, W.C., Martinoli, A.: Aggregation-mediated collective perception and action in a group of miniature robots. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 599–606 (2010)

    Google Scholar 

  24. Michel, O.: Cyberbotics LTD. webots\(^\text{ TM }\): professional mobile robot simulation. Int. J. Adva. Robot. Syst. 1(1), 5 (2004)

    Article  Google Scholar 

  25. Na, S., et al.: Bio-inspired artificial pheromone system for swarm robotics applications. Adapt. Behav. 1–21 (2020). https://doi.org/10.1177/1059712320918936

  26. 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 

  27. Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_2

    Chapter  Google Scholar 

  28. Schmickl, T.: How to engineer robotic organisms and swarms? In: Meng, Y., Jin, Y. (eds.) Bio-Inspired Self-Organizing Robotic Systems. Studies in Computational Intelligence, vol. 355, pp. 25–52. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20760-0_2

    Chapter  Google Scholar 

  29. Schmickl, T., Hamann, H.: BEECLUST: a swarm algorithm derived from honeybees. In: Bio-inspired Computing and Communication Networks, pp. 95–137 (2011)

    Google Scholar 

  30. Schmickl, T., Hamann, H., Wörn, H., Crailsheim, K.: Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Robot. Auton. Syst. 57(9), 913–921 (2009)

    Article  Google Scholar 

  31. Schmickl, T., et al.: Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agent. Multi-Agent Syst. 18(1), 133–155 (2009)

    Article  MathSciNet  Google Scholar 

  32. Soysal, O., Sahin, E.: Probabilistic aggregation strategies in swarm robotic systems. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 325–332. IEEE (2005)

    Google Scholar 

  33. Turgut, A.E., Çelikkanat, H., Gökçe, F., Şahin, E.: Self-organized flocking in mobile robot swarms. Swarm Intell. 2(2–4), 97–120 (2008)

    Article  Google Scholar 

  34. Vaughan, R.: Massively multi-robot simulation in stage. Swarm Intell. 2(2–4), 189–208 (2008)

    Article  Google Scholar 

Download references

Acknowledgment

This work was partially supported by the UK EPSRC RAIN (EP/R026084/1), RNE (EP/P01366X/1), and the Field of Excellence COLIBRI of the University of Graz projects.

Author information

Authors and Affiliations

  1. Swarm and Computational Intelligence Lab (SwaCIL), Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK

    Shiyi Wang, Barry Lennox & Farshad Arvin

  2. Department of Mechanical Engineering, Middle East Technical University, Ankara, Turkey

    Ali E. Turgut

  3. Artificial Life Lab, Institute of Biology, University of Graz, Graz, Austria

    Thomas Schmickl

Authors
  1. Shiyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali E. Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Schmickl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barry Lennox
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Farshad Arvin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farshad Arvin .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, S., Turgut, A.E., Schmickl, T., Lennox, B., Arvin, F. (2021). Investigation of Cue-Based Aggregation Behaviour in Complex Environments. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67540-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67539-4

  • Online ISBN: 978-3-030-67540-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation