Skip to main content

Advertisement

Log in

Path planning and task assignment of the multi-AUVs system based on the hybrid bio-inspired SOM algorithm with neural wave structure

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

A hybrid bio-inspired self-organizing map neural network algorithm is proposed for path planning and task assignment for a multi-autonomous underwater vehicle (AUV) system within a mixed (dynamic and static) three-dimensional (3D) environment. A 3D hybrid bio-inspired neural network model is established to represent the underwater environment and the distribution of the neuron pheromone content gradually diffusing, centered on the source point of the neural wave. Through self-regulation of the neural wave diffusion, the targets can achieve self-adaptive capabilities. “Multiple Newton interpolation” is used to identify the real target among interference targets, and the multi-AUV system transitions from tracking the false target to tracking the real target. Based on the principle of AUV individual kinematics, a velocity vector synthesis algorithm is proposed to overcome the interference of ocean currents. Simulation studies performed in five different environments demonstrate that the proposed algorithm has high adaptability, and the potential for wide application because its neural waves can be updated in real time.

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

Access this article

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
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Kulkarni IS, Pompili D (2010) Task allocation for networked autonomous underwater vehicles in critical missions. IEEE J Sel Areas Commun 28(5):716–727

    Article  Google Scholar 

  2. Rout R, Subudhi B (2016) A backstepping approach for the formation control of multiple autonomous underwater vehicles suing a leader-follower strategy. J Mar Eng Technol 15(1):38–46

    Article  Google Scholar 

  3. Zhu A, Yang SX (2006) A neural network approach to dynamic task assignment of multirobots. IEEE Trans Neural Networks 17(5):1278–1287

    Article  Google Scholar 

  4. Li JJ, Zhang RBX (2017) Multi-AUV distributed task allocation based on the differential evolution quantum bee colony optimization algorithm. Polish Marit Res 24(S3):65–71

    Article  Google Scholar 

  5. Yoon S, Qiao C (2007) A new search algorithm using autonomous and cooperative multiple sensor nodes. In: IEEE INFOCOM 2007—26th IEEE International Conference on Computer Communications, pp 937–945

  6. Yoon S, Qiao C (2011) Cooperative search and survey using autonomous underwater vehicles (AUV). IEEE Trans Parallel Distrib Syst 22(3):364–379

    Article  Google Scholar 

  7. Couillard M, Fawcett J, Davison M (2012) Optimizing constrained search patterns for remote mine-hunting vehicles. IEEE J Ocean Eng 37:75–84

    Article  Google Scholar 

  8. Cao X, Yu AL (2017) Multi-AUV cooperative target search algorithm in 3-D underwater workspace. J Navig 70(6):1293–1311

    Article  Google Scholar 

  9. Pyo J, Cho H, Yu S-C (2017) Beam slice-based recognition method for acoustic landmark with multi-beam forward looking sonar. IEEE Sens J 17(21):7074–7085

    Article  Google Scholar 

  10. Pyo J, Cho H, Yu, S-C (2017) Acoustic beam-based man-made underwater landmark detection method for Multi-beam sonar. In: IEEE Underwater Technology (UT), 21–24 February 2017

  11. Huang ZR, Zhu DQ (2015) A cooperative hunting algorithm of Multi-AUV in a 3-D dynamic environment. In: The 27th Chinese Control and Decision Conference (2015 CCDC), 23–25 May 2015

  12. Huang ZR, Zhu DQ, Sun B (2016) A Multi-AUV cooperative hunting method in a 3-D underwater environment with obstacle. Eng Appl Artif Intell 50:192–200

    Article  Google Scholar 

  13. Yan M, Zhu D, Yang SX (2013) A novel 3-D bio-inspired neural network model for the path planning of an AUV in underwater environments. Intell Autom Soft Comput 19(4):555–566

    Article  Google Scholar 

  14. Yang SX, Luo C (2004) A neural network approach to complete coverage path planning. IEEE Trans Syst Man Cybern B Cybern 34(1):718–724

    Article  Google Scholar 

  15. Sun B, Zhu DQ, Tian C, Luo CM (2019) Complete coverage autonomous underwater vehicles path planning based on glasius bio-inspired neural network algorithm for discrete and centralized programming. IEEE Trans Cognit Dev Syst 11(1):73–84

    Article  Google Scholar 

  16. Ren SX, Mei Y (2013) Underwater glider task allocation based on the ant colony algorithm. In: Ninth international conference on natural computation, pp 585–589

  17. Yu-Hsien L, Lin-Chin H, Shao-Y C, Chao-Ming Y (2018) The optimal route planning for inspection task of autonomous underwater vehicle composed of MOPSO-based dynamic routing algorithm in currents. Appl Ocean Res 75:178–192

    Article  Google Scholar 

  18. Aluizio AFR, Santana OV (2015) Self-organizing map with time-varying structure to plan and control artificial locomotion. IEEE Trans Neural Netw Learn Syst 26:1594–1607

    Article  MathSciNet  Google Scholar 

  19. Zhu A, Yang SX (2012) An improved SOM-based approach to dynamic task assignment of Multi-robots. In: World Congress on Intelligent Control and Automation, Jinan, China, July 2012, pp 2168–2173

  20. Zhu DQ, Huang H, Yang SX (2013) Dynamic task assignment and path planning of multi-AUV system based on an improved self-organizing map and velocity synthesis method in three-dimensional underwater workspace. IEEE Trans Cybern 43:504–514

    Article  Google Scholar 

  21. Zhu DQ, Liu Y, Sun B (2018) Task assignment and path planning of a multi-AUV system based on a glasius bio-inspired self- organising map algorithm. J Navig 71:482–496

    Article  Google Scholar 

  22. Cui RX, Gela SZS, How BVE, Choo YS (2010) Leader–follower formation control of underactuated autonomous underwater vehicles. Ocean Eng 1:1. https://doi.org/10.1016/j.oceaneng.2010.07.006

    Article  Google Scholar 

  23. Cao X, Zhu DQ, Simon XY (2016) Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. IEEE Trans Neural Netw Learn Syst 27(11):2364–2374

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This project is supported by Jilin Province Key Science and Technology R&D Project (Grant No: 20180201040GX), National Natural Science Foundation of China (Grant No: 51505174), Scientific and Technological Development Program of Jilin Province of China (Grant No: 20170101206JC), Foundation of Education Bureau of Jilin Province (Grant No: JJKH20170789KJ), Research Fund for the Doctoral Program of Higher Education of China (Grant No: 20130061120038), and National Key Research and Development Program of China (Grant No: 2017YFC0602002).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanli Chen.

Additional information

Technical Editor: Victor Juliano De Negri.

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

Ma, X., Chen, Y., Bai, G. et al. Path planning and task assignment of the multi-AUVs system based on the hybrid bio-inspired SOM algorithm with neural wave structure. J Braz. Soc. Mech. Sci. Eng. 43, 28 (2021). https://doi.org/10.1007/s40430-020-02733-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-020-02733-4

Keywords

Navigation