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Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence

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Abstract

Modern defense systems are developing towards systematization, intellectualization and automation, which include the collaborative defense system on the sea between multiple unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). UAVs can fly in high altitude and collect marine environment information on patrolling. Furthermore, UAVs can plan defense paths for USVs to intercept intruders with full-assignment or reassignment strategies aiming at maximum overall benefits. Thus, we propose dynamic overlay reconnaissance algorithm based on genetic idea (GI-DORA) to solve the problem of multi-UAV multi-station reconnaissance. Moreover, we develop continuous particle swarm optimization based on obstacle dimension (OD-CPSO) to optimize defense path of USVs to intercept intruders. In addition, under the designed defense constraints, we propose dispersed particle swarm optimization based on mutation and crossover (MC-DPSO) and real-time batch assignment algorithm (RTBA) in emergency for formulating combat defense mission assignment strategy in different scenarios. Finally, we illustrate the feasibility and effectiveness of the proposed methods.

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References

  1. CAO D S, SANG L Z. Key technologies of maritime distress search and rescue based on unmanned aerial vehicle [J]. Transport Research, 2017, 3(3): 62–67 (in Chinese).

    Google Scholar 

  2. WANG H L, WU G H, MA M H. Coordinated task planning method of multiple heterogeneous earth-observation platforms [J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(3): 997–1014 (in Chinese).

    Google Scholar 

  3. WANG H L, XIAO B, WU J, et al. A collaborative scheduling-based parallel solution for HEVC encoding on multicore platforms [J]. IEEE Transactions on Multimedia, 2018, 20(11): 2935–2948.

    Article  Google Scholar 

  4. AGHAEEYAN A, ABDOLLAHI F, TALEBI H A. Robust cooperative control in the presence of obstacles [C]// 21st Iranian Conference on Electrical Engineering. Mashhad: IEEE, 2013: 1–6.

    Google Scholar 

  5. AGHAEEYAN A, ABDOLLAHI F, TALEBI H A. UAV-UGVs cooperation: With a moving center based trajectory [J]. Robotics and Autonomous Systems, 2015, 63: 1–9.

    Article  Google Scholar 

  6. KAMEL M A, GHAMRY K A, ZHANG Y M. Fault tolerant cooperative control of multiple UAVs-UGVs under actuator faults [C]// 2015 International Conference on Unmanned Aircraft Systems. Denver, CO: IEEE, 2015: 644–649

    Chapter  Google Scholar 

  7. KAMEL M A, GHAMRY K A, ZHANG Y M. Real-time fault-tolerant cooperative control of multiple UAVs-UGVs in the presence of actuator faults [J]. Journal of Intelligent & Robotic Systems, 2016, 88(2/3/4): 469–480.

    Google Scholar 

  8. ZHANG J M, XIONG J F, ZHANG G Y, et al. Flooding disaster oriented USV & UAV system development & demonstration [C]// OCEANS 2016-Shanghai. Shanghai: IEEE, 2016: 1–4.

    Google Scholar 

  9. DUFEK J, MURPHY R. Visual pose estimation of USV from UAV to assist drowning victims recovery [C]// 14th International Symposium on Safety, Security and Rescue Robotics. Lausanne: IEEE, 2016: 147–153.

    Google Scholar 

  10. SHI W R, HUANG X H, ZHOU W. Path planning of mobile robot based on improved artificial potential field [J]. Journal of Computer Applications, 2010, 30(8): 2021–2023 (in Chinese).

    Article  Google Scholar 

  11. SABRI A N, RADZI N H M, SAMAH A A. A study on bee algorithm and A* algorithm for path finding in games [C]// 2018 IEEE Symposium on Computer Applications and Industrial Electronics. Penang Island: IEEE, 2018: 224–229.

    Chapter  Google Scholar 

  12. HUANG Y K. An improved shortest path algorithm based on Dijkstra for indoor mobile navigation [J]. Journal of Fujian Normal University (Natural Science Edition), 2017, 33(3): 13–18 (in Chinese).

    MATH  Google Scholar 

  13. LI H A, PENG Y Z, DENG C Y, et al. Review of hybrids of GA and PSO [J]. Computer Engineering and Applications, 2018, 54(2): 20–28 (in Chinese).

    Google Scholar 

  14. GUO S L, SUN H Y, CHEN Z. Path planning method for mobile robot based on improved genetic algorithm [J]. Electronics World, 2017 (6): 18–19 (in Chinese).

  15. LI W, WU W J, WANG H M, et al. Crowd intelligence in AI 2.0 era [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15–43.

    Article  Google Scholar 

  16. GUO Y, WANG W H, WU S T. Research on robot path planning based on fuzzy neural network and particle swarm optimization [C]// 29th Chinese Control and Decision Conference. Chongqing: IEEE, 2017: 2146–2150.

    Google Scholar 

  17. HUANG J, CHEN M, JIANG C S. Satisficing decision-making on task allocation for UAVs in air-to-ground attacking [J]. Electronics Optics & Control, 2014, 21(7): 10–13 (in Chinese).

    Google Scholar 

  18. XIONG S, YANG Y Q, NI M F. A new generalized assignment problem model and its method [J]. Advances in Computer Science Research, 2017, 54: 749–755.

    Google Scholar 

  19. LIANG Y S, WAN Z P, FANG D B. An improved artificial bee colony algorithm for solving constrained optimization problems [J]. International Journal of Machine Learning & Cybernetics, 2017, 8(3): 1–16.

    Article  Google Scholar 

  20. LI M, LIU W, ZHANG Y D. Mulit-agent dynamic task allocation based on improved contract net protocol [J]. Journal of Shandong University (Engineeing Science), 2016, 46(2): 51–56 (in Chinese).

    Google Scholar 

  21. WU J C, ZHOU R, RAN H M, et al. Performance comparison of genetic algorithm with auction algorithm in task allocation [J]. Electronics Optics & Control, 2016, 23(2): 11–15 (in Chinese).

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Xing Wu  (武 星), Yuan Liu  (刘 远) & Shaorong Xie  (谢少荣)

  2. Shanghai Institute for Advanced Communication and Data Science, Shanghai, 200444, China

    Xing Wu  (武 星)

  3. Department of Computing, Imperial College London, London, SW7 2AZ, UK

    Yike Guo  (郭毅可)

Authors
  1. Xing Wu  (武 星)
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  2. Yuan Liu  (刘 远)
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  3. Shaorong Xie  (谢少荣)
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  4. Yike Guo  (郭毅可)
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Corresponding author

Correspondence to Xing Wu  (武 星).

Additional information

Foundation item: The National Natural Science Foundation of China (No. 61625304)

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Wu, X., Liu, Y., Xie, S. et al. Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence. J. Shanghai Jiaotong Univ. (Sci.) 25, 51–56 (2020). https://doi.org/10.1007/s12204-019-2142-y

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  • DOI: https://doi.org/10.1007/s12204-019-2142-y

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