Abstract
In this paper, we present a new method for a swarm to collectively sense and follow a gradient in the environment. The agents in the swarm only rely on relative distance and bearing measurements of neighbors. Additionally, only a minority of agents in the swarm perceive the scalar value of the gradient at their location. We test the method with incrementally changing ratio of agents with sensors on various swarm sizes. In addition to repeated simulation experiments, we also test the performance with a real nano-drone swarm. Results show us that, using the new method, the swarm was successful at following the gradient in the environment even with a low portion of the swarm with sensors on various swarm sizes. A real nano-drone swarm also demonstrates a good performance in our test even with members having disabled sensors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amorim, T., Nascimento, T., Petracek, P., De Masi, G., Ferrante, E., Saska, M.: Self-organized UAV flocking based on proximal control. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1374–1382. IEEE (2021)
Berdahl, A., Torney, C.J., Ioannou, C.C., Faria, J.J., Couzin, I.D.: Emergent sensing of complex environments by mobile animal groups. Science 339(6119), 574–576 (2013)
Berdahl, A.M., et al.: Collective animal navigation and migratory culture: from theoretical models to empirical evidence. Philos. Trans. Roy. Soc. B Biol. Sci. 373(1746), 20170009 (2018)
Bjerknes, J.D., Winfield, A.F., Melhuish, C.: An analysis of emergent taxis in a wireless connected swarm of mobile robots. In: 2007 IEEE Swarm Intelligence Symposium, pp. 45–52. IEEE (2007)
Camley, B.A.: Collective gradient sensing and chemotaxis: modeling and recent developments. J. Phys.: Condens. Matter 30(22), 223001 (2018)
Couzin, I.D., Krause, J., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433(7025), 513–516 (2005). https://doi.org/10.1038/nature03236
Duisterhof, B.P., Li, S., Burgués, J., Reddi, V.J., de Croon, G.C.: Sniffy bug: a fully autonomous swarm of gas-seeking nano quadcopters in cluttered environments. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9099–9106. IEEE (2021)
Ferrante, E., Turgut, A.E., Stranieri, A., Pinciroli, C., Birattari, M., Dorigo, M.: A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat. Comput. 13(2), 225–245 (2014). https://doi.org/10.1007/s11047-013-9390-9
Karagüzel, T.A., Turgut, A.E., Eiben, A.E., Ferrante, E.: Collective gradient perception with a flying robot swarm. Swarm Intell. 17, 117–146 (2023). https://doi.org/10.1007/s11721-022-00220-1
Kernbach, S., Thenius, R., Kernbach, O., Schmickl, T.: Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic system. Adapt. Behav. 17(3), 237–259 (2009)
Olson, R.S., Hintze, A., Dyer, F.C., Knoester, D.B., Adami, C.: Predator confusion is sufficient to evolve swarming behaviour. J. Roy. Soc. Interface 10(85), 20130305 (2013). https://doi.org/10.1098/rsif.2013.0305
Preiss, J.A., Hönig, W., Sukhatme, G.S., Ayanian, N.: Crazyswarm: a large nano-quadcopter swarm. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3299–3304. IEEE (2017)
Puckett, J.G., Pokhrel, A.R., Giannini, J.A.: Collective gradient sensing in fish schools. Sci. Rep. 8(1), 1–11 (2018)
Schmickl, T., Wotawa, F., Thenius, R., Varughese, J.C.: FSTaxis algorithm: bio-inspired emergent gradient taxis. In: ALIFE 2016, The Fifteenth International Conference on the Synthesis and Simulation of Living Systems, pp. 330–337. MIT Press (2016)
Shaukat, M., Chitre, M.: Adaptive behaviors in multi-agent source localization using passive sensing. Adapt. Behav. 24(6), 446–463 (2016)
Valentini, G., Brambilla, D., Hamann, H., Dorigo, M.: Collective perception of environmental features in a robot swarm. In: Dorigo, M., et al. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 65–76. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44427-7_6
Vicsek, T., Zafeiris, A.: Collective motion. Phys. Rep. 517(3–4), 71–140 (2012)
Wahby, M., Petzold, J., Eschke, C., Schmickl, T., Hamann, H.: Collective change detection: adaptivity to dynamic swarm densities and light conditions in robot swarms. In: Artificial Life Conference Proceedings, pp. 642–649. MIT Press (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Karagüzel, T.A., Cambier, N., Eiben, A.E., Ferrante, E. (2024). Collective Gradient Following with Sensory Heterogeneous UAV Swarm. 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_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-51497-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51496-8
Online ISBN: 978-3-031-51497-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)