Skip to main content

Multi-UUV Underwater Target Cooperative Detection Task Planning and Assignment

  • Conference paper
  • First Online:
Bio-Inspired Computing: Theories and Applications (BIC-TA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1801))

  • 1049 Accesses

Abstract

Multi-UUV collaboration is critical for ocean exploration missions. Aiming at the problem of underwater wreck coverage detection, this paper designs a task allocation method based on the biological balance principle and a path planning strategy for underwater target UUV area coverage detection suitable for near-seabed environment. Voronori principle is used to divide the core detection area where the crash target wreckage is located, and then the UUV task load is balanced by the biological balance principle. According to the actual situation analysis of underwater vehicle coverage detection in near-seabed environment, combined with the intelligent unit reliability q function, the path planning strategy of underwater vehicle area coverage detection underwater target is designed. Simulation results show that the proposed algorithm can achieve multi-UUV underwater target detection task region allocation and achieve the consistency of load balancing among UUV tasks.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Xu, J., Zhou, X., Chen, X., et al.: Multi-scale adaptive corner detection and feature matching algorithm for UUV task target image. In: IEEE International Conference on Mechatronics and Automation, pp. 1104–1109, IEEE (2017)

    Google Scholar 

  2. Chen, Y.L., Ma, X.W., Bai, G.Q., et al.: Multi-autonomous underwater vehicle formation control and cluster search using a fusion control strategy at complex underwater environment. Ocean Eng. 216(7), 108048 (2020)

    Article  Google Scholar 

  3. Zhang, G.P., Liu, Z., Tian, X.D.: Searching and simulation of underwater cooperative mine detection based on UUV group. Ordnance Industry Automation (2007)

    Google Scholar 

  4. Zhu, D., Liu, Y., Sun, B.: Task assignment and path planning of a multi-AUV system based on a Glasius bio-inspired self-organising map algorithm. J. Navig. 71(2), 482–496 (2018)

    Article  Google Scholar 

  5. Deng, Y.: Task allocation and path planning for acoustic networks of AUVs. Florida Atlantic University (2010)

    Google Scholar 

  6. Du, X., Guo, Q., Li, H., et al.: Multi-UAVs cooperative task assignment and path planning scheme. J. Phys: Conf. Ser. 1856(1), 012016 (2021)

    Google Scholar 

  7. Lin, J., Pan, L.: Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system. Swarm Evol. Comput. 75, 101163 (2022)

    Article  Google Scholar 

  8. Deng, Y., Beaujean, P.J., An, E., et al.: Task allocation and path planning for collaborative autonomous underwater vehicles operating through an underwater acoustic network. J. Robot. 483095.1–483095.15 (2013)

    Google Scholar 

  9. Mahmoud Zadeh, S., Powers, D.M.W., Sammut, K., Yazdani, A.M.: A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment. Soft. Comput. 22(5), 1687–1710 (2016). https://doi.org/10.1007/s00500-016-2433-2

    Article  Google Scholar 

  10. Eun, Y., Bang, H.: Cooperative task assignment and path planning of multiple UAVs using genetic algorithm. In: AIAA Infotech@Aerospace 2007 Conference and Exhibit (2007)

    Google Scholar 

  11. Chen, X., Liu, Y.: Cooperative task assignment for multi-UAV attack mobile targets. In: 2019 Chinese Automation Congress (CAC) (2019)

    Google Scholar 

  12. Ghafoor, H., Noh, Y.: An overview of next-generation underwater target detection and tracking: an integrated underwater architecture. IEEE Access 99, 1 (2019)

    Google Scholar 

  13. Xu, J., Du, X., Li, J., et al.: Kernel two-dimensional nonnegative matrix factorization: a new method to target detection for UUV vision system. Complexity (2020)

    Google Scholar 

  14. Liu, C., Wang, H., Yingmin, G.U., et al.: UUV path planning method based on QPSO. In: Global Oceans 2020: Singapore - U.S. Gulf Coast (2020)

    Google Scholar 

  15. Li, Z., Li, S., Wen, S.: Automatic detection of an underwater target based on UUV. J. Harbin Eng. Univ. (2017)

    Google Scholar 

  16. Yang, F., Chakraborty, N.: Multirobot simultaneous path planning and task assignment on graphs with stochastic costs. In: 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) (2019)

    Google Scholar 

  17. Zhao, M., Li, T., Su, X.H., Zhao, L.L., Zhang, Y.H.: A survey on key issues of cooperative task planning for 3D multi-UAVs system. Intell. Comput. Appl. 6(1), 31–35 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yongzhou Lu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, Y., Zhou, H., Fang, H., Zhao, Z. (2023). Multi-UUV Underwater Target Cooperative Detection Task Planning and Assignment. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_38

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-1549-1_38

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1548-4

  • Online ISBN: 978-981-99-1549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics