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Characterization of the Design Space of Collective Braitenberg Vehicles

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Distributed Autonomous Robotic Systems (DARS 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 28))

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Abstract

Large collectives of artificial agents are quickly becoming a reality at the micro-scale for healthcare and biological research, and at the macro-scale for personal care, transportation, and environmental monitoring. However, the design space of reactive collectives and the resulting emergent behaviors are not well understood, especially with respect to different sensing models. Our work presents a well-defined model and simulation for study of such collectives, extending the Braitenberg Vehicle model to multi-agent systems with on-board stimulus. We define omnidirectional and directional sensing and stimulus models, and examine the impact of the modelling choices. We characterize the resulting behaviors with respect to spatial and kinetic energy metrics over the collective, and identify several behaviors that are robust to changes in the sensor model and other parameters. Finally, we provide a demonstration of how this approach can be used for control of a swarm using a single controllable agent and global mode switching.

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References

  1. Alicea, B., Dvoretskii, S., Felder, S., Gong, Z., Gupta, A., Parent, J.: Developmental embodied agents as meta-brain models. In: DevoNN Workshop (2020)

    Google Scholar 

  2. Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. MIT Press, Cambridge (1986)

    Google Scholar 

  3. Chen, J., Sun, R., Kress-Gazit, H.: Distributed control of robotic swarms from reactive high-level specifications. In: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp. 1247–1254. IEEE (2021)

    Google Scholar 

  4. Coppola, M., Guo, J., Gill, E., de Croon, G.C.: Provable self-organizing pattern formation by a swarm of robots with limited knowledge. Swarm Intell. 13(1), 59–94 (2019)

    Article  Google Scholar 

  5. Ding, T., et al.: Light-induced actuating nanotransducers. Proc. Natl. Acad. Sci. 113(20), 5503–5507 (2016)

    Article  Google Scholar 

  6. Dorigo, M., Theraulaz, G., Trianni, V.: Swarm robotics: past, present, and future [point of view]. Proc. IEEE 109(7), 1152–1165 (2021)

    Article  Google Scholar 

  7. Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence. Elsevier, Amsterdam (2001)

    Google Scholar 

  8. Frank, S., Kuijper, A.: Privacy by design: survey on capacitive proximity sensing as system of choice for driver vehicle interfaces. In: Computer Science in Cars Symposium, pp. 1–9 (2020)

    Google Scholar 

  9. Gardi, G., Ceron, S., Wang, W., Petersen, K., Sitti, M.: Microrobot collectives with reconfigurable morphologies, behaviors, and functions. Nat. Commun. 13(1), 1–14 (2022)

    Article  Google Scholar 

  10. Garnier, S., et al.: The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif. Life 14(4), 387–408 (2008)

    Article  Google Scholar 

  11. 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 

  12. Gautrais, J., Jost, C., Theraulaz, G.: Key behavioural factors in a self-organised fish school model. In: Annales Zoologici Fennici, vol. 45, pp. 415–428. BioOne (2008)

    Google Scholar 

  13. Hauert, S.: Swarm engineering across scales: from robots to nanomedicine. In: ECAL 2017, The Fourteenth European Conference on Artificial Life, pp. 11–12. MIT Press (2017)

    Google Scholar 

  14. Hogg, D.W., Martin, F., Resnick, M.: Braitenberg Creatures. Epistemology and Learning Group, MIT Media Laboratory Cambridge (1991)

    Google Scholar 

  15. achille hui. (https://math.stackexchange.com/users/59379/achille hui): Average distance between \(n\) randomly distributed points on a square with their nearest neighbors. Mathematics Stack Exchange. https://math.stackexchange.com/q/2565546. Accessed 11 Nov 2018

  16. Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(6), 988–1001 (2003)

    Article  MathSciNet  Google Scholar 

  17. Lanzisera, S., Zats, D., Pister, K.S.: Radio frequency time-of-flight distance measurement for low-cost wireless sensor localization. IEEE Sens. J. 11(3), 837–845 (2011)

    Article  Google Scholar 

  18. LaViers, A., et al.: Choreographic and somatic approaches for the development of expressive robotic systems. In: Arts, vol. 7, p. 11. MDPI (2018)

    Google Scholar 

  19. Lei, L., Escobedo, R., Sire, C., Theraulaz, G.: Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish. PLoS Comput. Biol. 16(3), e1007194 (2020)

    Article  Google Scholar 

  20. Li, S., et al.: Programming active cohesive granular matter with mechanically induced phase changes. Sci. Adv. 7(17), eabe8494 (2021)

    Article  Google Scholar 

  21. Mayya, S.: Local encounters in robot swarms: from localization to density regulation. Ph.D. thesis, Georgia Institute of Technology (2019)

    Google Scholar 

  22. McFassel, G., Shell, D.A.: Reactivity and statefulness: action-based sensors, plans, and necessary state. Int. J. Robot. Res., 02783649221078874 (2021)

    Google Scholar 

  23. Mitrano, P., Burklund, J., Giancola, M., Pinciroli, C.: A minimalistic approach to segregation in robot swarms. In: 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), pp. 105–111. IEEE (2019)

    Google Scholar 

  24. O’Keeffe, K.P., Hong, H., Strogatz, S.H.: Oscillators that sync and swarm. Nat. Commun. 8(1), 1–13 (2017)

    Article  Google Scholar 

  25. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 25–34 (1987)

    Google Scholar 

  26. Rezeck, P., Assunção, R.M., Chaimowicz, L.: Flocking-segregative swarming behaviors using Gibbs random fields. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 8757–8763. IEEE (2021)

    Google Scholar 

  27. Rezeck, P., Chaimowicz, L.: Chemistry-inspired pattern formation with robotic swarms. arXiv preprint: arXiv:2206.03388 (2022)

  28. Rosenthal, S.B., Twomey, C.R., Hartnett, A.T., Wu, H.S., Couzin, I.D.: Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl. Acad. Sci. 112(15), 4690–4695 (2015)

    Article  Google Scholar 

  29. Rueben, M., et al.: Themes and research directions in privacy-sensitive robotics. In: 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pp. 77–84. IEEE (2018)

    Google Scholar 

  30. Shahrokhi, S., Lin, L., Ertel, C., Wan, M., Becker, A.T.: Steering a swarm of particles using global inputs and swarm statistics. IEEE Trans. Rob. 34(1), 207–219 (2017)

    Article  Google Scholar 

  31. Thornton, C.: Predictive processing simplified: the infotropic machine. Brain Cogn. 112, 13–24 (2017)

    Article  Google Scholar 

  32. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226 (1995)

    Article  MathSciNet  Google Scholar 

  33. Wiseman, J.: Braitenberg vehicles (1999). http://people.cs.uchicago.edu/wiseman/vehicles/

  34. Yang, J.F., et al.: Memristor circuits for colloidal robotics: temporal access to memory, sensing, and actuation. Adv. Intell. Syst. 4(4), 2100205 (2022)

    Article  Google Scholar 

  35. Yu, J., LaValle, S.M., Liberzon, D.: Rendezvous without coordinates. IEEE Trans. Autom. Control 57(2), 421–434 (2011)

    MathSciNet  Google Scholar 

  36. Zardini, G., Censi, A., Frazzoli, E.: Co-design of autonomous systems: from hardware selection to control synthesis. In: 2021 European Control Conference (ECC), pp. 682–689. IEEE (2021)

    Google Scholar 

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Acknowledgments

This project was funded by the Packard Fellowship for Science and Engineering, GETTYLABS, and the National Science Foundation (NSF) Grant #2042411.

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Correspondence to Alexandra Q. Nilles .

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Defay, J.A., Nilles, A.Q., Petersen, K. (2024). Characterization of the Design Space of Collective Braitenberg Vehicles. In: Bourgeois, J., et al. Distributed Autonomous Robotic Systems. DARS 2022. Springer Proceedings in Advanced Robotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_19

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