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SCIENCE CHINA Information Sciences, Volume 63, Issue 4: 140312(2020) https://doi.org/10.1007/s11432-019-2763-5

Leader-following flocking for unmanned aerial vehicle swarm with distributed topology control

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  • ReceivedSep 26, 2019
  • AcceptedJan 10, 2020
  • PublishedMar 9, 2020

Abstract

To address the flocking issues of an unmanned aerial vehicle (UAV) swarm operating at a leader-follower mode, distributed control protocols comprising both kinetic controller and topology control algorithm must be implemented. For flocking the UAV swarm, a distributed control-input method is required forboth maintaining a relatively steady state between neighboring vehicles (including velocity matching and distance maintenance) and avoiding vehicle-to-vehicle collision. Furthermore, the stability of control protocols should be analyzed using the potential energy function. In particular, a distributed $\beta~$-angle test (BAT) rule in the proposed topology-control issue may allow each UAV to determine its neighboring set by exploiting the locally sensed information, thereby significantly reducing the communication overhead of the entire swarm. In addition, node-degree bound is derived to demonstrate the feasibility of the proposed algorithm, in which the optimal value in terms of convergence is analyzed. The flocking of the flying ad-hoc network (FANET) can be achieved in a self-organizing way without the use of an external control center via the distributed control protocols. Ultimately, the proposed analysis is verified by numerical results.


Acknowledgment

This work was supported in part by Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, Joint Foundation of the Ministry of Education (MoE) and China Mobile Group (Grant No. MCM20160103), and Beijing Institute of Technology Research Fund Program for Young Scholars.


References

[1] Bekmezci , Sahingoz O K, Temel . Flying Ad-Hoc Networks (FANETs): A survey. Ad Hoc Networks, 2013, 11: 1254-1270 CrossRef Google Scholar

[2] Hassanalian M, Abdelkefi A. Classifications, applications, and design challenges of drones: A review. Prog Aerospace Sci, 2017, 91: 99-131 CrossRef ADS Google Scholar

[3] Cardieri P. Modeling Interference in Wireless Ad Hoc Networks. IEEE Commun Surv Tutorials, 2010, 12: 551-572 CrossRef Google Scholar

[4] Xu J, Xu L, Xie L. Decentralized control for linear systems with multiple input channels. Sci China Inf Sci, 2019, 62: 52202 CrossRef Google Scholar

[5] Zhongshan Zhang , Keping Long , Jianping Wang . On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches. IEEE Commun Surv Tutorials, 2014, 16: 513-537 CrossRef Google Scholar

[6] Vicsek T, Czirók A, Ben-Jacob E. Novel Type of Phase Transition in a System of Self-Driven Particles. Phys Rev Lett, 1995, 75: 1226-1229 CrossRef PubMed ADS arXiv Google Scholar

[7] Duan H, Yang Q, Deng Y. Unmanned aerial systems coordinate target allocation based on wolf behaviors. Sci China Inf Sci, 2019, 62: 14201 CrossRef Google Scholar

[8] Rong B, Zhang Z, Zhao X. Robust Superimposed Training Designs for MIMO Relaying Systems Under General Power Constraints. IEEE Access, 2019, 7: 80404-80420 CrossRef Google Scholar

[9] Lu Z, Zhang L, Wang L. Controllability analysis of multi-agent systems with switching topology over finite fields. Sci China Inf Sci, 2019, 62: 12201 CrossRef Google Scholar

[10] Hildenbrandt H, Carere C, Hemelrijk C K. Self-organized aerial displays of thousands of starlings: a model. Behaval Ecol, 2010, 21: 1349-1359 CrossRef Google Scholar

[11] Reynolds C W. Flocks, herds and schools: A distributed behavioral model. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, 1987. 25-34. Google Scholar

[12] Nedic A, Olshevsky A, Rabbat M G. Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. Proc IEEE, 2018, 106: 953-976 CrossRef Google Scholar

[13] Santi P. Topology control in wireless ad hoc and sensor networks. ACM Comput Surv, 2005, 37: 164-194 CrossRef Google Scholar

[14] Jia Y, Li Q, Qiu S. Distributed Leader-Follower Flight Control for Large-Scale Clusters of Small Unmanned Aerial Vehicles. IEEE Access, 2018, 6: 32790-32799 CrossRef Google Scholar

[15] Jeng A A, Jan R. The r-Neighborhood Graph: An Adjustable Structure for Topology Control in Wireless Ad Hoc Networks. IEEE Trans Parallel Distrib Syst, 2007, 18: 536-549 CrossRef Google Scholar

[16] Young G F, Scardovi L, Cavagna A. Starling Flock Networks Manage Uncertainty in Consensus at Low Cost. PLoS Comput Biol, 2013, 9: e1002894 CrossRef PubMed ADS arXiv Google Scholar

[17] Blough Douglas M, Leoncini Mauro, Resta Giovanni, et al. The k-neigh protocol for symmetric topology control in ad hoc networks. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing. New York: ACM, 2003. 141--152. Google Scholar

[18] Chiwewe T M, Hancke G P. A Distributed Topology Control Technique for Low Interference and Energy Efficiency in Wireless Sensor Networks. IEEE Trans Ind Inf, 2012, 8: 11-19 CrossRef Google Scholar

[19] Tian B M, Yang H X, Li W. Optimal view angle in collective dynamics of self-propelled agents. Phys Rev E, 2009, 79: 052102 CrossRef PubMed ADS arXiv Google Scholar

[20] Shucker B, Bennett J K. Virtual Spring Mesh Algorithms for Control of Distributed Robotic Macrosensors. University of Colorado at Boulder, Technical Report CU-CS-996-05. 2005. Google Scholar

[21] Ning B, Han Q L, Zuo Z. Collective Behaviors of Mobile Robots Beyond the Nearest Neighbor Rules With Switching Topology.. IEEE Trans Cybern, 2018, 48: 1577-1590 CrossRef PubMed Google Scholar

[22] Li F, Chen Z M, Wang Y. Localized geometric topologies with bounded node degree for three-dimensional wireless sensor networks. EURASIP J Wirel Commun Netw, 2012, 2012: 157 DOI: 10.1109/MSN.2011.43. Google Scholar

[23] Bullo F, Cortes J, Martinez S. Distributed Control of Robotic Networks: a Mathematical Approach to Motion Coordination Algorithms. Princeton: Princeton University Press, 2009, 27. Google Scholar

[24] Godsil C, Royle G F. Algebraic Graph Theory. Berlin: Springer, 2013. Google Scholar

[25] Olfati-Saber R. Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. IEEE Trans Automat Contr, 2006, 51: 401-420 CrossRef Google Scholar

[26] Spencer Q H, Jeffs B D, Jensen M A. Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel. IEEE J Sel Areas Commun, 2000, 18: 347-360 CrossRef Google Scholar

[27] Rong P, Sichitiu M L. Angle of arrival localization for wireless sensor networks. In: Proceedings of the 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, Reston, 2006. 1: 374--382. Google Scholar

[28] Tanner H G, Jadbabaie A, Pappas G J. Flocking in Fixed and Switching Networks. IEEE Trans Automat Contr, 2007, 52: 863-868 CrossRef Google Scholar

[29] Shevitz D, Paden B. Lyapunov stability theory of nonsmooth systems. IEEE Trans Automat Contr, 1994, 39: 1910-1914 CrossRef Google Scholar

[30] Wang Y, Liu Y, Guo Z. Three-dimensional ocean sensor networks: A survey. J Ocean Univ China, 2012, 11: 436-450 CrossRef ADS Google Scholar

[31] Fiedler M. Algebraic connectivity of graphs. Czech Math J, 1973, 23: 298--305. Google Scholar

[32] Derr K, Manic M. Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms. IEEE Trans Ind Inf, 2013, 9: 1900-1911 CrossRef Google Scholar

  • Figure 5

    (Color online) $\gamma^*$ (a), $d^*$ (b), $v^*$ (c) and $E^*$ (d) under different $\beta$ at $N=20,~50,\text{~and~}80$.

  • Figure 6

    (Color online) Final state and trajectories of UAVs. (a) Final state of all UAVs; (b) trajectories of UAV swarm center and leader.

  • Figure 7

    (Color online) $\gamma^*$ (a), $d^*$ (b), $v^*$ (c) and $E^*$ (d) at each iteration.

  •   

    Algorithm 1 $\beta~$-angle test

    Require:neighbor position matrix ${\boldsymbol~p}_i$ for each UAV $i$, critical value of $\beta$;

    for $j\in~\mathcal{N}_i$

    for $k\in~\mathcal{N}_i~~\backslash~\{j\}~~$

    calculate $\angle~ikj$;

    if $\angle~ikj~>~\beta~$ then

    $a_{ij}=~0$;

    break

    end if

    $a_{ij}=~1$;

    end for

    end for

    return $A_i$.

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