Skip to main content
SpringerLink
Log in

Cooperative Mission Planning of USVs Based on Intention Recognition

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

To enhance task completion efficiency and quality, the coordination of Unmanned Surface Vehicle (USV) formations in complex environmental situations often requires user intervention. This paper proposes a human-machine collaborative approach for USV mission planning and explores a method for identifying user intervention intentions. A method for recognizing user intention based on intervention style was proposed. The method utilizes the Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) model to recognize intervention style and emphasizes human intention recognition to enhance the ability of USV in complex environments. The method involves modeling continuous intervention operations and incorporating intervention style features to accurately identify user intent. The study proposes a fusion method that combines feature attention, self-attention, and Fusion of Long Short-Term Memory Networks (FLSTMS) to achieve its purpose. Furthermore, it suggests a cooperative mission planning method based on prospect theory, which integrates user risk propensity and identified intentions to optimize planning. Simulation experiments confirm the effectiveness of this approach, highlighting its advantages over traditional methods.

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

Access this article

Log in via an institution

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
Algorithm 1:
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Liu C et al (2024) Overcoming data limitations: a few-shot specific Emitter Identification Method using self-supervised learning and adversarial augmentation. IEEE Trans Inf Forensics Secur 19:500–513. https://doi.org/10.1109/TIFS.2023.3324394

    Article  Google Scholar 

  2. Zheng Z, Bashir AK (2022) Graph-enabled intelligent vehicular network data processing. IEEE Trans Intell Transp Syst 23(5):4726–4735. https://doi.org/10.1109/TITS.2022.3158045

    Article  Google Scholar 

  3. Losey DP, McDonald CG, Battaglia E, O’Malley MK (2018) A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction. Appl Mech Rev 70(1):010804. https://doi.org/10.1115/1.4039145

    Article  Google Scholar 

  4. Yao Z et al (2023) Few-shot specific emitter identification using asymmetric masked auto-encoder. IEEE Commun Lett 27(10):2657–2661. https://doi.org/10.1109/LCOMM.2023.3312669

    Article  Google Scholar 

  5. Lin Y et al (2020) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Trans Cogn Commun Netw 99:1–1. https://doi.org/10.1109/TCCN.2020.3024610

    Article  Google Scholar 

  6. Zheng Z et al (2023) Parallel overlapping community detection algorithm on GPU. IEEE Trans Big Data 9:677–687. https://doi.org/10.1109/TBDATA.2022.3180360

    Article  Google Scholar 

  7. Xuan Q, Chen Z, Liu Y, Huang H, Bao G, Zhang D (2019) Multiview generative adversarial network and its application in pearl classification. IEEE Trans Industr Electron 66(10):8244–8252. https://doi.org/10.1109/TIE.2017.2784394

    Article  Google Scholar 

  8. Liu B, Wei XL, Qu H (2023) Research on human-drone collaborative air combat maneuver decision making. Aeronaut Eng Progress 14(06):63–72. https://doi.org/10.16615/j.cnki.1674-8190.2023.06.07

    Article  Google Scholar 

  9. Sun X, Li S, Zhang Y (2022) MAV-UAV collaborative route planning based on intelligent emotion mode. Proceedings of International Conference on Autonomous Unmanned Systems (ICAUS 2021), vol 861, pp 2744–2754. https://doi.org/10.1007/978-981-16-9492-9_269

  10. Li Y, Han W, Wang Y (2020) Deep reinforcement learning with application to air confrontation intelligent decision-making of manned/unmanned aerial vehicle cooperative system. IEEE Access 8:67887–67898. https://doi.org/10.1109/ACCESS.2020.2985576

    Article  Google Scholar 

  11. Zhang R, Wen N, Yu H, Wang L, Wu J, Liu G (2019) USVs cooperative collision avoidance based on man-machine interaction and the artificial potential field method. 2019 Chinese Automation Congress (CAC), Hangzhou, China, pp 1552–1557. https://doi.org/10.1109/CAC48633.2019.8997031

  12. Wu X, Liu K, Zhang J (2021) An optimized collision avoidance decision-making system for autonomous ships under human-machine cooperation situations. J Adv Transp 2021:1–17. https://doi.org/10.1155/2021/7537825

    Article  Google Scholar 

  13. Yan P, Lei G, Li XM (2019) Research status and development trends of unmanned surface vessels. J Shanghai Univ (Natural Science Edition) 25(05): 645–654. https://doi.org/10.12066/j.issn.1007-2861.2041

  14. Shen C, Li L, Wu Y (2018) Research on the Development of the United States’ Airborne Manned/Unmanned Autonomous Collaborative Combat Capability. Tactical Missile Technol 2018(01): 16–21. https://doi.org/10.16358/j.issn.1009-1300.2018.01.03

  15. Liu Y, Zhao P, Qin D, Li G, Chen Z, Zhang Y (2019) Driving intention identification based on long short-term memory and a case study in shifting Strategy optimization. IEEE Access 7:128593–128605. https://doi.org/10.1109/ACCESS.2019.2940114

    Article  Google Scholar 

  16. Mao YS, Peng W, Xiang ZQ, Song LF, Liu MX (2021) Collision avoidance algorithm of unmanned surface vehicle based on target interference intent recognition. J Dalian Maritime Univ 47(2):26–34. https://doi.org/10.16411/j.cnki.issn1006-7736.2021.02.004

    Article  Google Scholar 

  17. Zhang Z, Wang H, Geng J (2022) An information fusion method based on deep learning and fuzzy discount-weighting for target intention recognition. Eng Appl Artif Intell 109:104610. https://doi.org/10.1016/j.engappai.2021.10461

    Article  Google Scholar 

  18. Wang J et al (2024) Sparse bayesian learning-based 3-D radio environment map construction—sampling optimization, scenario-dependent dictionary construction, and sparse recovery. IEEE Trans Cogn Commun Netw 10(1):80–93. https://doi.org/10.1109/TCCN.2023.3319539

    Article  Google Scholar 

  19. Na Z, Liu Y, Shi J, Liu C, Gao Z (2021) UAV-supported clustered NOMA for 6G-enabled internet of things: trajectory planning and resource allocation. IEEE Internet Things J 8(20):15041–15048. https://doi.org/10.1109/JIOT.2020.3004432

    Article  Google Scholar 

  20. Huang J, Li Y, Wu SB (2022) Driving style recognition based on multivariate feature parameters and improved SVM algorithm. J Chongqing Univ Technol (Natural Science) 36(11):8–19

    Google Scholar 

  21. Wang Y, Li W, Jiang R (2022) A novel hybrid algorithm based on improved particle swarm optimization algorithm and genetic algorithm for multi-UAV path planning with time windows. 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp 1005–1009. https://doi.org/10.1109/IMCEC55388.2022.10019937

  22. Zhao CD, Zhuang JH, Cheng XM (2022) RNN-Bi-LSTM ship trajectory prediction based on feature attention mechanism. J Guangdong Ocean Univ 42(05):102–109

    Google Scholar 

  23. Gao Z, Bao M, Gao F, Tang M (2023) Probabilistic multi-modal expected trajectory prediction based on LSTM for autonomous driving. Proc Inst Mech Eng Part D: J Automobile Eng. https://doi.org/10.1177/09544070231167906

    Article  Google Scholar 

  24. Kekelia N, Esiava E (2023) Utility theory and mathematical analysis of risk attitude. Collect Sci Works Sokhumi Univ 19. https://doi.org/10.52340/sou.2023.19.53

  25. Wang JJ, Ma XL, Xu H (2022) A three-way decision method with prospect theory to multi-attribute decision-making and its applications under hesitant fuzzy environments. Appl Soft Comput 126:109283. https://doi.org/10.1016/j.asoc.2022.109283

    Article  Google Scholar 

  26. Kahneman D, Tversky A (2013) Prospect theory: an analysis of decision under risk [M]. Handbook of the fundamentals of financial decision making, pp 99–127. https://doi.org/10.2307/1914185

  27. Mei X, Han D, Saeed N (2022) Trajectory optimization of autonomous surface vehicles with outliers for underwater target localization. Remote Sens 14(17):4343. https://doi.org/10.3390/rs14174343

    Article  Google Scholar 

  28. Hu JY, Li GW, Jia QL (2020) Multi-target attack decision-making in time-sensitive missions based on artificial potential field and entropy-AHP method. Adv Guidance Navig Control 644:2993–3005. https://doi.org/10.1007/978-981-15-8155-7_250

    Article  Google Scholar 

  29. Zhang YY, Qu D, Ke J, Li XM (2017) Dynamic obstacle avoidance for USV based on velocity obstacle and dynamic window method. J Shanghai Univ (Natural Science Edition) 23(1):1–16. https://doi.org/10.3969/j.issn.1007-2861.2016.07.021

    Article  Google Scholar 

  30. Wang H, Wang J, Ding G, Chen J, Gao F, Han Z (2019) Completion time minimization with path planning for fixed-wing UAV communications. IEEE Trans Wireless Commun 18(7):3485–3499. https://doi.org/10.1109/TWC.2019.2914203

    Article  Google Scholar 

  31. Yao P, Lou Y, Zhang K (2023) Multi-USV cooperative path planning by window update based self-organizing map and spectral clustering. Ocean Eng 275:114140. https://doi.org/10.1016/j.oceaneng.2023.114140

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the anonymous reviewers and editors of this paper for their valuable comments.

Funding

This research was funded by the Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology under grant number JCKYS2022SXJQR-10.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Changting Shi, Yanqiang Wang, Jing Shen & Junhui Qi

  2. Science and Technology on Underwater Vehicle Technology Laboratory, Harbin, 150001, China

    Changting Shi & Junhui Qi

Authors
  1. Changting Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanqiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junhui Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.S. contributed to the design and writing of the study, and the author supervised the study and advised on the revision of the manual and provided input on the revision of the draft manuscript. Y.W. contributed to the data. J.S. made some comments on the manuscript. J.Q. provided some review for the revision of the manuscript. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jing Shen.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

The picture materials quoted in this article have no copyright requirements, and the source has been indicated.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, C., Wang, Y., Shen, J. et al. Cooperative Mission Planning of USVs Based on Intention Recognition. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02324-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-024-02324-w

Keywords

Search

Navigation