Abstract
This study investigates the behavior of a system of identical robots modeled as self-propelled particles using the Vicsek model. The focus is on understanding the emergence of a chimera state, which represents the spontaneous emergence of multitasking capabilities in the robot swarm. The study considers scenarios with limited communication in a minimal system of 3 identical robots. Numerical simulations and stability analysis are conducted to analyze the system’s characteristics and motion patterns. The research aims to provide insights into the underlying mechanisms and develop a comprehensive framework for controlling and understanding the emergence of the chimera state in robot swarm systems. Additionally, the study examines the influence of coupling strength and phase lag on the occurrence of chimera states through direct numerical simulations and theoretical analysis. The results reveal the relationship between system structure and the manifestation of chimera states, providing valuable insights for further research in this field.
This work was supported by China Postdoctoral Science Foundation (Grant No. 2022M712927), Zhejiang Lab Open Research Project (No. K2022NB0AB01), and Key Research Project of Zhejiang Lab (No. G2021NB0AL03).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dorigo, M., Theraulaz, G., Trianni, V.: Reflections on the future of swarm robotics. Sci. Robot. 5(49), eabe4385 (2020)
Nedjah, N., Junior, L.S.: Review of methodologies and tasks in swarm robotics towards standardization. Swarm Evol. Comput. 50, 100565 (2019)
Wen, J., He, L., Zhu, F.: Swarm robotics control and communications: imminent challenges for next generation smart logistics. IEEE Commun. Mag. 56(7), 102–107 (2018)
Li, Z., Barenji, A.V., Jiang, J., Zhong, R.Y., Xu, G.: A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand. J. Intell. Manuf. 31(2), 469–480 (2020)
Kouzehgar, M., Meghjani, M., Bouffanais, R.: Multi-agent reinforcement learning for dynamic ocean monitoring by a swarm of buoys. In: Global Oceans,: Singapore-US Gulf Coast. IEEE 2020, 1–8 (2020)
Agarwala, N.: Monitoring the ocean environment using robotic systems: advancements, trends, and challenges. Mar. Technol. Soc. J. 54(5), 42–60 (2020)
Schranz, M., Umlauft, M., Sende, M., Elmenreich, W.: Swarm robotic behaviors and current applications. Front. Robot. AI 7, 36 (2020)
Kim, K., Kim, H., Myung, H.: Bio-inspired robot swarm control algorithm for dynamic environment monitoring. Adv. Robot. Res. 2(1), 1 (2018)
Ji, K., Zhang, Q., Yuan, Z., Cheng, H., Yu, D.: A virtual force interaction scheme for multi-robot environment monitoring. Robot. Auton. Syst. 149, 103967 (2022)
Zheng, J., Yang, T., Liu, H., Su, T., Wan, L.: Accurate detection and localization of unmanned aerial vehicle swarms-enabled mobile edge computing system. IEEE Trans. Industr. Inf. 17(7), 5059–5067 (2020)
Zhou, W., Liu, Z., Li, J., Xu, X., Shen, L.: Multi-target tracking for unmanned aerial vehicle swarms using deep reinforcement learning. Neurocomputing 466, 285–297 (2021)
Tahir, A., Böling, J., Haghbayan, M.-H., Toivonen, H.T., Plosila, J.: Swarms of unmanned aerial vehicles-a survey. J. Ind. Inf. Integr. 16, 100106 (2019)
Zhou, C., et al.: The review unmanned surface vehicle path planning: based on multi-modality constraint. Ocean Eng. 200, 107043 (2020)
Sauter, J.A., Bixler, K.: Design of unmanned swarm tactics for an urban mission," in Unmanned Systems Technology XXI, vol. 11021. SPIE, pp. 124–139 (2019)
Che, G., Liu, L., Yu, Z.: An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle. J. Ambient. Intell. Humaniz. Comput. 11(8), 3349–3354 (2020)
Herlambang, T., Rahmalia, D., Yulianto, T.: Particle swarm optimization (pso) and ant colony optimization (aco) for optimizing pid parameters on autonomous underwater vehicle (auv) control system. J. Phys. Conf. Ser. 1211(1). IOP Publishing, 2019, p. 012039
Vedachalam, N., Ramesh, R., Jyothi, V.B.N., Prakash, D., Ramadass, G.: Autonomous underwater vehicles-challenging developments and technological maturity towards strategic swarm robotics systems. Marine Georesources Geotechnol. 37(5), 525–538 (2019)
Berlinger, F., Gauci, M., Nagpal, R.: Implicit coordination for 3d underwater collective behaviors in a fish-inspired robot swarm. Sci. Robot. 6(50), eabd8668 (2021)
Rubenstein, M., Cornejo, A., Nagpal, R.: Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–799 (2014)
Zhou, X., et al.: Swarm of micro flying robots in the wild. Sci. Robot. 7(66), eabm5954 (2022)
Li, S., et al.: Particle robotics based on statistical mechanics of loosely coupled components. Nature 567(7748), 361–365 (2019)
Wang, G., et al.: Emergent field-driven robot swarm states. Phys. Rev. Lett. 126(10), 108002 (2021)
Kruk, N., Maistrenko, Y., Koeppl, H.: Self-propelled chimeras. Phys. Rev. E 98(3), 032219 (2018)
Kuramoto, Y., Battogtokh, D.: Coexistence of coherence and incoherence in nonlocally coupled phase oscillators, arXiv preprint cond-mat/0210694 (2002)
Abrams, D.M., Strogatz, S.H.: Chimera states for coupled oscillators. Phys. Rev. Lett. 93(17), 174102 (2004)
Parastesh, F., et al.: Chimeras. Phys. Rep. 898, 1–114 (2021)
Zhang, J., et al.: Spontaneous emergence of multitasking robotic swarms. In: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp. 184–188 (2022)
Chaté, H., Ginelli, F., Grégoire, G., Peruani, F., Raynaud, F.: Modeling collective motion: variations on the vicsek model. Eur. Phys. J. B 64(3), 451–456 (2008)
Ginelli, F.: The physics of the vicsek model. Eur. Phys. J. Special Top. 225(11), 2099–2117 (2016)
Gong, C.C., Zheng, C., Toenjes, R., Pikovsky, A.: Repulsively coupled kuramoto-sakaguchi phase oscillators ensemble subject to common noise. Chaos Interdisciplinary J. Nonlinear Sci. 29(3), 033127 (2019)
Mihara, A., Medrano-T, R.O.: Stability in the kuramoto-sakaguchi model for finite networks of identical oscillators. Nonlinear Dyn. 98(1), 539–550 (2019)
Ha, S.-Y., Xiao, Q.: Nonlinear instability of the incoherent state for the kuramoto-sakaguchi-fokker-plank equation. J. Stat. Phys. 160, 477–496 (2015)
Abhyankar, S., et al.: Petsc/ts: A modern scalable ode/dae solver library. arXiv preprint arXiv:1806.01437 (2018)
Zhang, H., Constantinescu, E.M., Smith, B.F.: Petsc tsadjoint: a discrete adjoint ode solver for first-order and second-order sensitivity analysis. arXiv preprint arXiv:1912.07696 (2019)
Zhang, Y., Motter, A.E.: Mechanism for strong chimeras. Phys. Rev. Lett. 126(9), 094101 (2021)
Ashwin, P., Burylko, O.: Weak chimeras in minimal networks of coupled phase oscillators. Chaos Interdisciplinary J. Nonlinear Sci. 25(1), 013106 (2015)
Burylko, O., Martens, E.A., Bick, C.: Symmetry breaking yields chimeras in two small populations of kuramoto-type oscillators. Chaos Interdisciplinary J. Nonlinear Sci. 32(9), 093109 (2022)
Kotwal, T., Jiang, X., Abrams, D.M.: Connecting the kuramoto model and the chimera state. Phys. Rev. Lett. 119(26), 264101 (2017)
Kohar, V., Ji, P., Choudhary, A., Sinha, S., Kurths, J.: Synchronization in time-varying networks. Phys. Rev. E 90(2), 022812 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, J., Li, H., Du, H., Liang, Y., Song, W., Li, T. (2023). Spontaneous Emergence of Multitasking in Minimal Robotic Systems. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14273. Springer, Singapore. https://doi.org/10.1007/978-981-99-6498-7_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-6498-7_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6497-0
Online ISBN: 978-981-99-6498-7
eBook Packages: Computer ScienceComputer Science (R0)