SpringerLink
Log in

Bio-inspired self-organized cooperative control consensus for crowded UUV swarm based on adaptive dynamic interaction topology

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Cooperative control is currently a challenging topic of crowded unmanned underwater vehicle (UUV) swarm. However, individual behavior conflict and chain-avalanche collision involved in this swarm are easily triggered due to the fluctuations and disturbances. In order to address the two problems, a bio-inspired self-organized cooperative control consensus derived from adaptive dynamic interaction topology is investigated in this paper. Firstly, a novel following-interaction framework incorporating the topological interaction and visual interaction is devised to ensure the minimum number and optimal distribution for neighborhoods. Then, an adaptive dynamic computing model inspired by single-nearest-neighbor following and weighted- multiple-nearest-neighbors following is proposed to steer a sensitive following behavior, in which the influence of each individual on this following behavior is described by a nonlinear weight. Finally, a distributed control protocol is put forward by using the proposed following model and mathematics-based potential fields to achieve the cohesive flocking and avoiding collision, and its sufficient conditions is proven by Laypunov and LaSalle invariance principle to accomplish a self- organized cooperative control. Simulation results are presented for illustrating the feasibility and effectiveness of our proposed control approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig.2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Wu Y, Low KH, Lv C (2020) Cooperative Path Planning for Heterogeneous Unmanned Vehicles in a Search-and-Track Mission Aiming at an Underwater Target. IEEE Trans Veh Technol 69(6):6782–6787

    Article  Google Scholar 

  2. Londhe PS, Patre BM (2019) Adaptive fuzzy sliding mode control for robust trajectory tracking control of an autonomous underwater vehicle. Intell Serv Robot 12:87–102

    Article  Google Scholar 

  3. Ingrand F, Ghallab M (2017) Deliberation for autonomous robots: a survey. Artif Intell 247:10–14

    Article  MathSciNet  Google Scholar 

  4. Bukhari AC, Kim YG (2013) A research on an intelligent multipurpose fuzzy semantic enhanced 3D virtual reality simulator for complex maritime missions. Appl Intell 38:193–209

    Article  Google Scholar 

  5. Liang HT, Qiang N (2020) Distributed Cooperative Control Based on Dynamic Following Interaction Mechanism for UUV Swarm. 2020 39th Chinese control conference (CCC), Shenyang, China, pp 5092–5097

    Google Scholar 

  6. Oh H, Shirazi AR, Sun CL, ** YC (2017) Bio-inspired self-organising multi-robot pattern formation: a review. Robot Auton Syst 91:83–100

    Article  Google Scholar 

  7. Ferrante E, Turgut AE, Huepe C, Stranieri A, Pinciroli C, Dorigo M (2012) Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt Behav 20(6):460–477

    Article  Google Scholar 

  8. Pandey P, Pompili D, Yi J (2015) Dynamic collaboration between networked robots and clouds in resource-constrained environments. IEEE Trans Autom Sci Eng 12(2):471–480

    Article  Google Scholar 

  9. Wang J, Wang C, Wei Y, Zhang C (2020) Neuroadaptive sliding mode formation control of autonomous underwater vehicles with uncertain dynamics. IEEE Syst J 14(3):3325–3333

    Article  Google Scholar 

  10. Sahu BK, Subudhi B (2018) Flocking Control of Multiple AUVs Based on Fuzzy Potential Functions. IEEE Trans Fuzzy Syst 26(5):2539–2551

    Article  Google Scholar 

  11. Yang H, Zhang F (2012) Robust control of formation dynamics for autonomous underwater vehicles in horizontal plane. J Dyn Syst Meas Control 134:031009

    Article  Google Scholar 

  12. Pan W, Jiang D, Pang Y, Qi Y, Luo D. Distributed Formation Control of Autonomous Underwater Vehicles Based on Flocking and Consensus Algorithms. In: Huang Y, Wu H, Liu H, Yin Z (eds) Intelligent robotics and applications. ICIRA 2017. Lecture Notes in Computer Science, vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_68

  13. Chen YY, Zhu DQ (2020) Research on the Method of Multi-AUV Formation Control Based on Self-organized Artificial Potential Filed. Control Eng China 26(10):1875–1881

    Google Scholar 

  14. Hu J, Wu Y, Li T, Ghosh BK (2019) Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Trans Autom Control 64(5):2122–2127

    Article  MathSciNet  Google Scholar 

  15. Cai YL, Zhang HG, Liang YL, Gao ZY (2020) Reduced-order observer-based robust leader-following control of heterogeneous discrete-time multi-agent systems with system uncertainties. Appl Intell 50:1794–1812

    Article  Google Scholar 

  16. Maupong TM, Rapisard P (2017) Data-driven control: a behavioral approach. Syst Control Lett 101:37–43

    Article  MathSciNet  Google Scholar 

  17. Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioral model. Comput Graph 21(4):25–34

  18. Couzin ID, Krause J, Franks NR (2005) Effective leadership and decision-making in animal groups on the move. Nature 433:513–516

    Article  Google Scholar 

  19. Vicsek T, Zafeiris A (2012) Collective motion. Phys Rep 517:71–140

    Article  Google Scholar 

  20. Aldana M, Dossetti V, Huepe C (2007) Phase transitions in systems of self-propelled agents and related network models. Phys Rev Lett 98:095702

    Article  Google Scholar 

  21. Liu MY, Lei XK, Yang PP (2014) Progress of theoretical modelling and empirical studies on collective motion. Chin Sci Bull 59:2464–2483

    Article  Google Scholar 

  22. Grünbaum D, Viscido S, Parrish JK (2005) Extracting interactive control algorithms from group dynamics of schooling fish. Coop Control 309:103–117

  23. Nagy M, Vásárhelyi G, Pettit B, Mariani R, Vicsek T, Biro D (2013) Context-dependent hierarchies in pigeons. Proc Natl Acad Sci 110:13049–13054

    Article  Google Scholar 

  24. Conradt L (2012) Models in animal collective decision-making: Information uncertainty and conflicting preferences. Interface Focus 2:226–240

    Article  Google Scholar 

  25. Anderson JR (2004) Cognitive psychology and its implications. Worth Publishers, New York

    Google Scholar 

  26. Qiu HX, Duan HB (2020) A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inf Sci 509:515–529

    Article  MathSciNet  Google Scholar 

  27. Liang HT, Fu YF, Kang FJ, Gao J, Ning Q (2020) A Behavior-driven Coordination Control Framework for Target Hunting by UUV Intelligent Swarm. IEEE Access 8(1):4838–4859

    Article  Google Scholar 

  28. Yang PP, Liu MY, Lei XK, Song C (2016) A novel control algorithm for the self-organized fission behavior of flocking system with time delay. Int J Control Autom Syst 14(4):986–997

    Article  Google Scholar 

  29. Khaldi B, Harrou F, Cherif F, Sun Y (2020) Improving robots swarm aggregation performance through the Minkowski distance function. 6th international conference on mechatronics and robotics engineering (ICMRE), Barcelona, Spain, pp 87–91

    Google Scholar 

  30. Chen C, Chen G, Guo L (2017) On the minimum number of neighbors needed for consensus of flocks. Control Theory Technol 15:327–339

    Article  MathSciNet  Google Scholar 

  31. Massé B, Ba S, Horaud R (2018) Tracking gaze and visual focus of attention of people involved in social interaction. IEEE Trans Pattern Anal Mach Intell 40(11):2711–2724

    Article  Google Scholar 

  32. Herbert JE, Perna A, Mann RP, Schaerf TM, Sumpter DJT, Ward AJW (2011) Inferring the rules of interaction of shoaling fish. Proc Natl Acad Sci 108:18726–18731

    Article  Google Scholar 

  33. Duan H, Huo M, Shi Y (2020) Limit-cycle-based mutant multiobjective pigeon-inspired optimization. IEEE Trans Evol Comput 24(5):948–959

    Article  Google Scholar 

  34. Katz Y, Tunstrøm K, Ioannou CC, Huepe C, Couzin ID (2011) Inferring the structure and dynamics of interactions in schooling fish. Proc Natl Acad Sci 108:1870–1872

    Article  Google Scholar 

  35. Godsil C, Royle G (2001) Algebraic graph theory. Springer-Verlag, Berlin

    Book  Google Scholar 

  36. Yan ZP, Liu YB, Zhou JJ, Zhang W, Wang L (2017) Consensus of multiple autonomous underwater vehicles with double independent Markovian switching topologies and timevarying delays. Chin Phys B 26(4):040203

    Article  Google Scholar 

  37. Zhang XY, Jia SM, Li XZ (2017) Improving the synchronization speed of self-propelled particles with restricted vision via randomly changing the line of sight. Nonlinear Dyn 90:43–51

    Article  Google Scholar 

  38. Li P, Duan HB (2019) A flocking model based on selective attention mechanics. Sci Sin Technol 49(9):1040–1050

    Article  Google Scholar 

  39. Yang PP, Tang Y, Song JC (2018) Self-organized fission/fusion method for flocking system based on predictive intelligence. Control Decis 33(12):2270–2276

    Google Scholar 

  40. Dai S, He S, Lin H, Wang C (2018) Platoon formation control with prescribed performance guarantees for USVs. IEEE Trans Ind Electron 65(5):4237–4246

    Article  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the financial support from the National Natural Science Foundation of China under Grant 11404205, Natural Science Foundation of Shaanxi under Grant 2019JQ-026 and Fundamental Research Funds for Central Universities under Grant GK201903016 and GK201803023. And the authors would like to thank all reviewers and editors who provided extensive valuable feedback.

Author information

Authors and Affiliations

  1. School of Physics and Information Technology, Shaanxi Normal University, **an, 710119, China

    Hongtao Liang & Jie Gao

  2. School of Computer Science and Engineering, **’an Technological University, **’an, 710032, China

    Yanfang Fu

Authors
  1. Hongtao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanfang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongtao Liang.

Ethics declarations

Conflict of interest

This work is original research and approved by all authors. The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, H., Fu, Y. & Gao, J. Bio-inspired self-organized cooperative control consensus for crowded UUV swarm based on adaptive dynamic interaction topology. Appl Intell 51, 4664–4681 (2021). https://doi.org/10.1007/s10489-020-02104-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-02104-5

Keywords

Search

Navigation