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Indirect Swarm Control: Characterization and Analysis of Emergent Swarm Behaviors (2309.11408v2)

Published 20 Sep 2023 in cs.RO, cs.SY, and eess.SY

Abstract: Emergence and emergent behaviors are often defined as cases where changes in local interactions between agents at a lower level effectively changes what occurs in the higher level of the system (i.e., the whole swarm) and its properties. However, the manner in which these collective emergent behaviors self-organize is less understood. The focus of this paper is in presenting a new framework for characterizing the conditions that lead to different macrostates and how to predict/analyze their macroscopic properties, allowing us to indirectly engineer the same behaviors from the bottom up by tuning their environmental conditions rather than local interaction rules. We then apply this framework to a simple system of binary sensing and acting agents as an example to see if a re-framing of this swarms problem can help us push the state of the art forward. By first creating some working definitions of macrostates in a particular swarm system, we show how agent-based modeling may be combined with control theory to enable a generalized understanding of controllable emergent processes without needing to simulate everything. Whereas phase diagrams can generally only be created through Monte Carlo simulations or sweeping through ranges of parameters in a simulator, we develop closed-form functions that can immediately produce them revealing an infinite set of swarm parameter combinations that can lead to a specifically chosen self-organized behavior. While the exact methods are still under development, we believe simply laying out a potential path towards solutions that have evaded our traditional methods using a novel method is worth considering. Our results are characterized through both simulations and real experiments on ground robots.

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References (34)
  1. G. Zhang, G. Fricke, and D. Garg, “Spill detection and perimeter surveillance via distributed swarming agents,” IEEE/asme Transactions on Mechatronics, vol. 18, no. 1, pp. 121–129, 2011.
  2. H. Kuntze, C. Frey, I.Tchouchenkov, B. Staehle, E. Rome, K. Pfeiffer, A. Wenzel, and J. Wöllenstein, “Seneka-sensor network with mobile robots for disaster management,” in 2012 IEEE Conference on Technologies for Homeland Security (HST), pp. 406–410, IEEE, 2012.
  3. M. Saska, J. Chudoba, L. Přeučil, J. Thomas, G. Loianno, A. Třešňák, V. Vonásek, and V. Kumar, “Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance,” in 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 584–595, IEEE, 2014.
  4. R. Arnold, J. Jablonski, B. Abruzzo, and E. Mezzacappa, “Heterogeneous uav multi-role swarming behaviors for search and rescue,” in 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 122–128, IEEE, 2020.
  5. J. Fromm, “Types and forms of emergence,” arXiv preprint nlin/0506028, 2005.
  6. J. Fromm, “Ten questions about emergence,” arXiv preprint nlin/0509049, 2005.
  7. O. Holland, “Taxonomy for the modeling and simulation of emergent behavior systems,” in Proceedings of the 2007 spring simulation multiconference-Volume 2, pp. 28–35, 2007.
  8. A. Özdemir, M. Gauci, A. Kolling, M. Hall, and R. Groß, “Spatial coverage without computation,” in 2019 International Conference on Robotics and Automation (ICRA), pp. 9674–9680, IEEE, 2019.
  9. M. Gauci, J. Chen, W. Li, T. Dodd, and R. Groß, “Self-organized aggregation without computation,” The International Journal of Robotics Research, vol. 33, no. 8, pp. 1145–1161, 2014.
  10. D. St-Onge, C. Pinciroli, and G. Beltrame, “Circle formation with computation-free robots shows emergent behavioral structure,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5344–5349, IEEE, 2018.
  11. M. Gauci, J. Chen, W. Li, T. Dodd, and R. Groß, “Clustering objects with robots that do not compute,” in Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, pp. 421–428, 2014.
  12. A. Alfeo, M. Cimino, N. D. Francesco, M. Lega, and G. Vaglini, “Design and simulation of the emergent behavior of small drones swarming for distributed target localization,” Journal of computational science, vol. 29, pp. 19–33, 2018.
  13. Y. Chuang, Y. R. Huang, M. R. D’Orsogna, and A. L. Bertozzi, “Multi-vehicle flocking: scalability of cooperative control algorithms using pairwise potentials,” in Proceedings 2007 IEEE international conference on robotics and automation, pp. 2292–2299, IEEE, 2007.
  14. A. Özdemir, M. Gauci, and R. Groß, “Shepherding with robots that do not compute,” in Artificial Life Conference Proceedings, pp. 332–339, MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info …, 2017.
  15. Y. Meng, H. Guo, and Y. Jin, “A morphogenetic approach to flexible and robust shape formation for swarm robotic systems,” Robotics and Autonomous Systems, vol. 61, no. 1, pp. 25–38, 2013.
  16. M. Rubenstein, A. Cabrera, J. Werfel, G. Habibi, J. McLurkin, and R. Nagpal, “Collective transport of complex objects by simple robots: theory and experiments,” in Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, pp. 47–54, 2013.
  17. H. Sayama, “Swarm chemistry,” Artificial life, vol. 15, no. 1, pp. 105–114, 2009.
  18. H. Sayama, “Seeking open-ended evolution in swarm chemistry,” in 2011 IEEE Symposium on Artificial Life (ALIFE), pp. 186–193, IEEE, 2011.
  19. H. Sayama, “Morphologies of self-organizing swarms in 3d swarm chemistry,” in Proceedings of the 14th annual conference on Genetic and evolutionary computation, pp. 577–584, 2012.
  20. Springer Science & Business Media, 2013.
  21. Wikipedia, “Phase diagram,” https://en.wikipedia.org/wiki/Phase_diagram, 2002.
  22. A. Costanzo and C. K. Hemelrijk, “Spontaneous emergence of milling (vortex state) in a vicsek-like model,” Journal of Physics D: Applied Physics, vol. 51, no. 13, p. 134004, 2018.
  23. M. Gauci, J. Chen, T. J. Dodd, and R. Groß, “Evolving aggregation behaviors in multi-robot systems with binary sensors,” in Distributed Autonomous Robotic Systems (M. Ani Hsieh and G. Chirikjian, eds.), (Berlin, Heidelberg), pp. 355–367, Springer Berlin Heidelberg, 2014.
  24. D. S. Brown, R. Turner, O. Hennigh, and S. Loscalzo, “Discovery and exploration of novel swarm behaviors given limited robot capabilities,” in Distributed Autonomous Robotic Systems, Springer International Publishing AG, 2018.
  25. F. Berlinger, M. Gauci, and R. Nagpal, “Implicit coordination for 3d underwater collective behaviors in a fish-inspired robot swarm,” Science Robotics, vol. 6, no. 50, 2021.
  26. M. Gauci, J. Chen, T. J. Dodd, and R. Groß, “Evolving aggregation behaviors in multi-robot systems with binary sensors,” in Distributed Autonomous Robotic Systems: The 11th International Symposium, pp. 355–367, Springer, 2014.
  27. C. Shalizi and C. Moore, “What is a macrostate? subjective observations and objective dynamics,” arXiv preprint cond-mat/0303625, 2003.
  28. H. Haeri, K. Jerath, and J. Leachman, “Thermodynamics-inspired macroscopic states of bounded swarms,” ASME Letters in Dynamic Systems and Control, vol. 1, no. 1, p. 011015, 2021.
  29. C. Taylor, C. Luzzi, and C. Nowzari, “On the effects of collision avoidance on emergent swarm behavior,” in 2020 American Control Conference (ACC), pp. 931–936, IEEE, 2020.
  30. Princeton University Press, 2009.
  31. U. Wilensky, NetLogo (and NetLogo User Manual). Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/, 1999.
  32. J. G. Leishman, “Introduction to aerospace flight vehicles,” 2022.
  33. R. Vega, K. Zhu, S. Luke, M. Parsa, and C. Nowzari, “Simulate less, expect more: Bringing robot swarms to life via low-fidelity simulations,” arXiv preprint arXiv:2301.09018, 2023.
  34. S. Luke, K. Andrea, M. Bowen, D. Fleming, K. Sullivan, B. Hrolenok, C. Vo, A. Bovill, R. Steck, B. Davidson, et al., “The flockbots,” Open specification. Available at http://cs. gmu. edu/  eclab/projects/robots/flockbots, 2014.
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