Abstract
The paper deals with a biologically inspired model of self-propelled particles introduced by Vicsek. To solve the problem of low convergence efficiency in this model, an improved model based on distance weight is proposed in this paper. Particularly, distance weight function is designed in the form of polynomial function which is a monotone increasing function of distance. Moreover, a new index to evaluate the convergence efficiency called Vicsek algebraic connectivity is promoted. Finally, comprehensive comparative studies of the convergence properties among the improved model, original Vicsek model, and Degree model are investigated in the simulation part. The simulation results show that our modified model is better than other two models in convergence probability and consensus time. Our results may enlighten other researchers in revealing the mechanism of collective motion.
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References
Reid, C.R., Latty, T.: Collective behaviour and swarm intelligence in slime moulds. FEMS Microbiol. Rev. 40(6), 798–806 (2016)
Kim, D.H., Shin, S.: Self-organization of decentralized swarm agents based on modified particle swarm algorithm. J. Intell. Robot. Syst. 46(2), 129–149 (2006)
Yasuda, T., Ohkura, K.: Collective behavior acquisition of real robotic swarms using deep reinforcement learning. In: 2018 2nd IEEE International Conference on Robotic Computing (IRC), pp. 179–180. IEEE (2018)
Moarref, S., Kress-Gazit, H.: Decentralized control of robotic swarms from high-level temporal logic specifications. In: 2017 International Symposium on Multi-robot and Multi-agent Systems (MRS), pp. 17–23. IEEE (2017)
Vásárhelyi, G., Virágh, C., Somorjai, G., Nepusz, T., Eiben, A.E., Vicsek, T.: Optimized flocking of autonomous drones in confined environments. Sci. Robot. 3(20), eaat3536 (2018)
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)
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)
Barberis, L., Peruani, F.: Large-scale patterns in a minimal cognitive flocking model: incidental leaders, nematic patterns, and aggregates. Phys. Rev. Lett. 117(24), 248001 (2016)
Shirazi, M.J., Abaid, N.: Collective behavior in groups of self-propelled particles with active and passive sensing inspired by animal echolocation. Phys. Rev. E 98(4), 042404 (2018)
Yang, H., Ci, L., Zhang, F., Yang, M., Mao, Y., Niu, K.: MR-APG: an improved model for swarm intelligence movement coordination. In: International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, pp. 223–230. Springer (2018)
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)
Savkin, A.V.: Coordinated collective motion of groups of autonomous mobile robots: analysis of Vicsek’s model. IEEE Trans. Autom. Control 49(6), 981–982 (2004)
Huepe, C., Aldana, M.: Intermittency and clustering in a system of self-driven particles. Phys. Rev. Lett. 92(16), 168701 (2004)
Tian, B.M., Yang, H.X., Li, W., Wang, W.X., Wang, B.H., Zhou, T.: Optimal view angle in collective dynamics of self-propelled agents. Phys. Rev. E 79(5), 052102 (2009)
Gao, J., Chen, Z., Cai, Y., Xu, X.: Approach to enhance convergence efficiency of Vicsek model. Control Decis. 24(8) (2009)
George, M., Ghose, D.: Reducing convergence times of self-propelled swarms via modified nearest neighbor rules. Phys. A: Stat. Mech. Its Appl. 391(16), 4121–4127 (2012)
Kim, Y., Mesbahi, M.: On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian. In: Proceedings of the 2005, American Control Conference, 2005, pp. 99–103. IEEE (2005)
Acknowledgements
This work was funded by the Innovation Academy for Light-duty Gas Turbine, Chinese Academy of Sciences under Grant No. CXYJJ19-ZD-02.
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Liu, B., Pu, Z., Wu, S., Shi, L., Wang, L., Yang, W. (2022). Improved Self-Propelled Swarms Model with Enhanced Convergence Efficiency. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_407
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DOI: https://doi.org/10.1007/978-981-15-8155-7_407
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