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Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Xuejing Lan, Wenbiao Xu, Zhijia Zhao and Guiyun Liu, "Autonomous Control Strategy of a Swarm System Under Attack Based on Projected View and Light Transmittance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 648-655, Mar. 2021. doi: 10.1109/JAS.2021.1003880
Citation: Xuejing Lan, Wenbiao Xu, Zhijia Zhao and Guiyun Liu, "Autonomous Control Strategy of a Swarm System Under Attack Based on Projected View and Light Transmittance," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 648-655, Mar. 2021. doi: 10.1109/JAS.2021.1003880

Autonomous Control Strategy of a Swarm System Under Attack Based on Projected View and Light Transmittance

doi: 10.1109/JAS.2021.1003880
Funds:  This work was supported in part by the National Natural Science Foundation of China (61803111, 61803109), in part by the Innovative School Project of Education Department of Guangdong (2017KQNCX153), in part by the Science and Technology Planning Project of Guangzhou City (201904010494), in part by the Scientific Research Projects of Guangzhou Education Bureau (201831805, 202032793)
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  • In the study of a visual projection field with swarm movements, an autonomous control strategy is presented in this paper for a swarm system under attack. To ensure a fast swarm dynamic response and stable spatial cohesion in a complex environment, a new hybrid swarm motion model is proposed by introducing global visual projection information to a traditional local interaction mechanism. In the face of attackers, individuals move towards the largest free space according to the projected view of the environment, rather than directly in the opposite direction of the attacker. Moreover, swarm individuals can certainly regroup without dispersion after the attacker leaves. On the other hand, the light transmittance of each individual is defined based on global visual projection information to represent its spatial freedom and relative position in the swarm. Then, an autonomous control strategy with adaptive parameters is proposed according to light transmittance to guide the movement of swarm individuals. The simulation results demonstrate in detail how individuals can avoid attackers safely and reconstruct ordered states of swarm motion.

     

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  • [1]
    O. Feinerman, I. Pinkoviezky, A. Gelblum, E. Fonio, and N. S. Gov, “The physics of cooperative transport in groups of ants,” Nature Physics, vol. 14, pp. 683–693, Jul. 2018.
    [2]
    A. M. Calvão and E. Brigatti, “Collective movement in alarmed animals groups: A simple model with positional forces and a limited attention field,” Physica A, vol. 520, pp. 450–457, Apr. 2019. doi: 10.1016/j.physa.2019.01.029
    [3]
    H. Ling, G. E. Mclvor, J. Westley, K. Vaart, R. T. Vaughan, A. Thornton, and N. T. Ouellette, “Behavioural plasticity and the transition to order in jackdaw flocks,” Nature Communications, vol. 10, no. 1, pp. 1–7, Nov. 2019.
    [4]
    H. Ling, G. E. Mclvor, K. van der Vaart, R. T. Vaughan, A. Thornton, and N. T. Ouellette, “Local interactions and their group-level consequences in flocking jackdaws,” Proc. the Royal Society B:Biological Sciences, vol. 286, no. 1906, pp. 1–10, Jul. 2019. doi: 10.1098/rspb.2019.0865
    [5]
    I. L. Bajec and F. H. Heppner, “Organized flight in birds,” Animal Behaviour, vol. 78, no. 4, pp. 777–789, Oct. 2009. doi: 10.1016/j.anbehav.2009.07.007
    [6]
    S. D. Algar, T. Lymburn, T. Stemler, M. Small, and T. Jüngling, “Learned emergence in selfish collective motion,” Chaos:An Interdisciplinary Journal of Nonlinear Science, vol. 29, no. 12, pp. 1–11, Dec. 2019. doi: 10.1063/1.5120776
    [7]
    B. Zhu, L. Xie, D. Han, X. Meng, and R. Teo, “A survey on recent progress in control of swarm systems,” Science China Information Sciences, vol. 60, no. 7, pp. 1–24, Jun. 2017.
    [8]
    P. Rahmani, F. Peruani, and P. Romanczuk, “Flocking in complex environments—Attention trade-offs in collective information processing,” PLOS Computational Biology, vol. 16, no. 4, pp. 1–18, Apr. 2020.
    [9]
    W. Zha, J. Chen, and Z. Peng, “Dynamic multi-team antagonistic games model with incomplete information and its application to multi-UAV,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 1, pp. 74–84, 2015. doi: 10.1109/JAS.2015.7032908
    [10]
    H. Oh, A. R. Shirazi, C. Sun, and Y. Jin, “Bio-inspired self-organising multi-robot pattern formation: A review,” Robotics and Autonomous Systems, vol. 2017, no. 91, pp. 83–100, Jan. 2017.
    [11]
    X. Huang and J. Dong, “Reliable leader-to-follower formation control of multiagent systems under communication quantization and attacks,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 50, no. 1, pp. 1–11, Jan. 2020.
    [12]
    L. Huang, M. C. Zhou, K. Hao, and E. Hou, “A survey of multi-robot regular and adversarial patrolling,” IEEE/CAA Journal of Automatica Sinica, vol. 6, pp. 894–903, 2019. doi: 10.1109/JAS.2019.1911537
    [13]
    A. Yang, W. Naeem, and M. Fei, “Decentralised formation control and stability analysis for multi vehicle cooperative manoeuvre,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 1, pp. 92–100, 2014.
    [14]
    L. Bayindir, “A review of swarm robotics tasks,” Neurocomputing, vol. 172, no. C, pp. 292–321, Jan. 2016.
    [15]
    X. Huang and J. Dong, “ADP-Based Robust Resilient Control of Partially Unknown Nonlinear Systems via Cooperative Interaction Design,” IEEE Trans. Systems,Man,and Cybernetics:Systems, pp. 1–9, Feb. 2020.
    [16]
    C. W. Reynolds, “Flocks, herds and schools – A distributed behavioral model,” SIGGRAPH, vol. 1987, no. 21, pp. 25–34, 1987.
    [17]
    T. Vicsek, A. Czirok, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical Review Letters, vol. 75, pp. 1226–1229, Aug. 1995. doi: 10.1103/PhysRevLett.75.1226
    [18]
    I. D. Couzin, J. Krause, R. James, G. D. Ruxton, and N. R. Franks, “Collective memory and spatial sorting in animal groups,” Journal of Theoretical Biology, vol. 218, pp. 1–11, Sep. 2002. doi: 10.1006/jtbi.2002.3065
    [19]
    F. Cucker and S. Smale, “Emergent behavior in flocks,” IEEE Trans. Automatic Control, vol. 52, no. 5, pp. 852–862, May 2007. doi: 10.1109/TAC.2007.895842
    [20]
    A. Attanasi, A. Cavagna, L. Del Castello, I. Giardina, T. S. Grigera, A. Jelic, S. Melillo, L. Parisi, O. Pohl, E. Shen, and M. Viale, “Information transfer and behavioural inertia in starling flocks,” Nature Physics, vol. 10, no. 9, pp. 691–696, Jul. 2014. doi: 10.1038/nphys3035
    [21]
    B. Li, Z. X. Wu, and J. Y. Guan, “Collective motion patterns of selfpropelled agents with both velocity alignment and aggregation interactions,” Physical Review E, vol. 99, no. 2, pp. 1–10, Feb. 2019.
    [22]
    R. Olfati-Saber, “Flocking for multi-agent dynamic systems – algorithms and theory,” IEEE Trans. Automatic Control, vol. 51, no. 3, pp. 401–420, 2006. doi: 10.1109/TAC.2005.864190
    [23]
    X. Lan, L. Liu, and Y. Wang, “ADP-based intelligent decentralized control for multi-agent systems moving in obstacle environment,” IEEE Access, vol. 7, pp. 59 624–59 630, May 2019. doi: 10.1109/ACCESS.2019.2914669
    [24]
    X. Lan, Y. Liu, and Z. Zhao, “Cooperative control for swarming systems based on reinforcement learning in unknown dynamic environment,” Neurocomputing, vol. 410, pp. 410–418, Oct. 2020. doi: 10.1016/j.neucom.2020.06.038
    [25]
    C. C. Cheah, S. P. Hou, and J. J. E. Slotine, “Region-based shape control for a swarm of robots,” Automatica, vol. 45, no. 10, pp. 2406–2411, Oct. 2009. doi: 10.1016/j.automatica.2009.06.026
    [26]
    X. Lan, Z. Wu, W. Xu, and G. Liu, “Adaptive-neural-network-based shape control for a swarm of robots,” Complexity, vol. 2018, pp. 1–8, Dec. 2018.
    [27]
    A. Cavagna, A. Cimarelli, I. Giardina, G. Parisi, R. Santagati, F. Stefanini, and M. Viale, “Scale-free correlations in starling flocks,” Proc. the National Academy of Sciences, vol. 107, no. 26, pp. 11865–11 870, 2010. doi: 10.1073/pnas.1005766107
    [28]
    M. Zumaya, H. Larralde, and M. Aldana, “Delay in the dispersal of flocks moving in unbounded space using long-range interactions,” Scientific Reports, vol. 8, pp. 1–9, Oct. 2018. doi: 10.1038/s41598-017-17765-5
    [29]
    J. Ren, W. Sun, D. Manocha, A. Li, and X. Jin, “Stable information transfer network facilitates the emergence of collective behavior of bird flocks,” Physical Review E, vol. 98, no. 5, pp. 1–10, Nov. 2018.
    [30]
    H. Ling, G. E. Mclvor, J. Westley, K. van der Vaart, J. Yin, R. T. Vaughan, A. Thornton, and N. T. Ouellette, “Collective turns in jackdaw flocks: kinematics and information transfer,” Journal of The Royal Society Interface, vol. 16, no. 159, pp. 1–10, Oct. 2019. doi: 10.1098/rsif.2019.0450
    [31]
    A. Cavagna, I. Giardina, T. S. Grigera, A. Jelic, D. Levine, S. Ramaswamy, and M. Viale, “Silent Flocks: Constraints on Signal Propagation Across Biological Groups,” Physical Review Letters, vol. 114, no. 21, pp. 1–5, May 2015.
    [32]
    A. Strandburg-Peshkin, C. R. Twomey, N. W. F. Bode, A. B. Kao, Y. Katz, C. C. Ioannou, S. B. Rosenthal, C. J. Torney, H. S. Wu, S. A. Levin, and I. D. Couzin, “Visual sensory networks and effective information transfer in animal groups,” Current Biology, vol. 23, no. 17, pp. R709–R711, Sep. 2013. doi: 10.1016/j.cub.2013.07.059
    [33]
    D. J. G. Pearce, A. M. Miller, G. Rowlands, and M. S. Turner, “Role of projection in the control of bird flocks,” Proc. the National Academy of Sciences, vol. 111, no. 29, pp. 10422–10426, Jul. 2014. doi: 10.1073/pnas.1402202111
    [34]
    B. Collignon, A. Sguret, and J. Halloy, “A stochastic vision-based model inspired by zebrafish collective behaviour in heterogeneous environments,” Royal Society Open Science, vol. 3, no. 1, Article no. 150473, 2016.
    [35]
    H. J. Charlesworth and M. S. Turner, “Intrinsically motivated collective motion,” Proc. the National Academy of Sciences, vol. 116, no. 31, pp. 15 362–15 367, 2019. doi: 10.1073/pnas.1822069116
    [36]
    L. Barberis and F. Peruani, “Large-scale patterns in a minimal cognitive flocking model: incidental leaders, nematic patterns, and aggregates,” Physical Review Letters, vol. 117, no. 24, pp. 248001–248006, Dec. 2016. doi: 10.1103/PhysRevLett.117.248001
    [37]
    T. Bagarti and S. N. Menon, “Milling and meandering: Flocking dynamics of stochastically interacting agents with a field of view,” Physical Review E, vol. 100, no. 1, pp. 1–6, Jul. 2019.
    [38]
    F. A. Lavergne, H. Wendehenne, T. Bauerle, and C. Bechinger, “Group formation and cohesion of active particles with visual perceptiondependent motility,” Science, vol. 364, pp. 70–74, Apr. 2019. doi: 10.1126/science.aau5347
    [39]
    R. Bastien and P. Romanczuk, “A model of collective behavior based purely on vision,” Science Advances, vol. 6, pp. 1–9, Feb. 2020.
    [40]
    M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic, “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,” Proc. the National Academy of Sciences, vol. 105, no. 4, pp. 1232–1237, 2008. doi: 10.1073/pnas.0711437105

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    Highlights

    • A hybrid swarm motion model is proposed with the visual projection information.
    • The light transmittance of each individual is defined based on the projected view.
    • An autonomous control strategy is presented according to the light transmittance.

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