Volume 50 Issue 12
Dec.  2024
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WU Q S,GUO J,KANG Z L,et al. Maritime mission assignment of UAV clusters based on γ random search strategy[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(12):3872-3883 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0882
Citation: WU Q S,GUO J,KANG Z L,et al. Maritime mission assignment of UAV clusters based on γ random search strategy[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(12):3872-3883 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0882

Maritime mission assignment of UAV clusters based on γ random search strategy

doi: 10.13700/j.bh.1001-5965.2022.0882
Funds:

Shanghai Aerospace Science and Technology Innovation Fund (SAST201711) 

More Information
  • Corresponding author: E-mail:guojie1981@bit.edu.cn
  • Received Date: 03 Nov 2022
  • Accepted Date: 23 Dec 2022
  • Available Online: 13 Jan 2023
  • Publish Date: 10 Jan 2023
  • In view of the characteristics of complex maritime combat situations, diverse combat missions, and heterogeneous combat units of unmanned aerial vehicle (UAV) clusters, a multi-objective mission assignment optimization model for maritime UAV clusters was established, and an improved discrete particle swarm optimization algorithm based on $\gamma $ random search strategy (γ-DPSO) was proposed for this model. Firstly, the combat situation details and complex combat requirements were introduced into the mission assignment problem of UAV clusters, and a mission assignment combat model of UAV clusters that fitted the combat scenario was established. Secondly, based on the particle coding matrix, the equilibrium search strategy, the $\gamma $ random search strategy, and the phased adaptive parameters were designed, and the improved discrete particle swarm optimization algorithm based on the $\gamma $ random search strategy was proposed to solve the problem that the discrete particle swarm optimization algorithm was easy to fall into local optimum and caused immature convergence. The simulation results show that the proposed improved algorithm can effectively solve the multi-objective mission assignment problem of UAV clusters for the multi-objective mission assignment optimization model of UAV clusters established in this paper that meets the characteristics of maritime combat, and the proposed improved strategy improves the convergence speed and accuracy of the algorithm.

     

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  • [1]
    谢伟, 陶浩, 龚俊斌, 等. 海上无人系统集群发展现状及关键技术研究进展[J]. 中国舰船研究, 2021, 16(1): 7-17.

    XIE W, TAO H, GONG J B, et al. Research advances in the development status and key technology of unmanned marine vehicle swarm operation[J]. Chinese Journal of Ship Research, 2021, 16(1): 7-17(in Chinese).
    [2]
    刘丽, 武坦然, 邵东青. 美军空中无人作战概念解析[J]. 航天电子对抗, 2022, 38(1): 26-30. doi: 10.3969/j.issn.1673-2421.2022.01.006

    LIU L, WU T R, SHAO D Q. Analysis of the combat concept of unmanned aerial system of the US armed forces[J]. Aerospace Electronic Warfare, 2022, 38(1): 26-30(in Chinese). doi: 10.3969/j.issn.1673-2421.2022.01.006
    [3]
    王宇, 郭兴旺. 无人系统集群海上作战应用研究[J]. 舰船电子工程, 2019, 39(12): 21-25.

    WANG Y, GUO X W. Research on the application of unmanned system cluster in marine combat applications[J]. Ship Electronic Engineering, 2019, 39(12): 21-25(in Chinese).
    [4]
    吴子沉, 胡斌. 基于态势认知的无人机集群围捕方法[J]. 北京亚洲成人在线一二三四五六区学报, 2021, 47(2): 424-430.

    WU Z C, HU B. Swarm rounding up method of UAV based on situation cognition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 424-430(in Chinese).
    [5]
    李桂亮, 毕海洋, 洪雪健, 等. 基于DE-DPSO-GT-SA算法的协同多任务分配[J]. 北京亚洲成人在线一二三四五六区学报, 2021, 47(1): 90-96.

    LI G L, BI H Y, HONG X J, et al. Cooperative multi-task assignment based on DE-DPSO-GT-SA algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 90-96(in Chinese).
    [6]
    梁天骄, 陈晓明, 杨朝旭, 等. 舰载无人机滑行轨迹控制方法[J]. 北京亚洲成人在线一二三四五六区学报, 2021, 47(2): 289-296.

    LIANG T J, CHEN X M, YANG Z X, et al. Trajectory control method for unmanned carrier aircraft taxiing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 289-296(in Chinese).
    [7]
    张令, 段海滨, 雍婷, 等. 基于寒鸦配对交互行为的无人机集群编队控制[J]. 北京亚洲成人在线一二三四五六区学报, 2021, 47(2): 391-397.

    ZHANG L, DUAN H B, YONG T, et al. Unmanned aerial vehicle swarm formation control based on paired interaction mechanism in jackdaws[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 391-397(in Chinese).
    [8]
    符小卫, 陈子浩. 多无人机协同探测快速目标的控制方法设计[J]. 系统工程与电子技术, 2021, 43(11): 3295-3304. doi: 10.12305/j.issn.1001-506X.2021.11.30

    FU X W, CHEN Z H. Design of control method for multi-UAV cooperative detection of fast target[J]. Systems Engineering and Electronics, 2021, 43(11): 3295-3304(in Chinese). doi: 10.12305/j.issn.1001-506X.2021.11.30
    [9]
    郭继峰, 郑红星, 贾涛, 等. 异构无人系统协同作战关键技术综述[J]. 宇航学报, 2020, 41(6): 686-696. doi: 10.3873/j.issn.1000-1328.2020.06.006

    GUO J F, ZHENG H X, JIA T, et al. Summary of key technologies for heterogeneous unmanned system cooperative operations[J]. Journal of Astronautics, 2020, 41(6): 686-696(in Chinese). doi: 10.3873/j.issn.1000-1328.2020.06.006
    [10]
    GAO S, WU J Z, AI J L. Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm[J]. Soft Computing, 2021, 25(10): 7155-7167. doi: 10.1007/s00500-021-05675-8
    [11]
    KIM J, OH H, YU B, et al. Optimal task assignment for UAV swarm operations in hostile environments[J]. International Journal of Aeronautical and Space Sciences, 2021, 22(2): 456-467. doi: 10.1007/s42405-020-00317-z
    [12]
    HUO L, ZHU J, WU G, et al. A novel simulated annealing based strategy for balanced UAV task assignment and path planning[J]. Sensors, 2020, 20(17): 4769. doi: 10.3390/s20174769
    [13]
    王然然, 魏文领, 杨铭超, 等. 考虑协同航路规划的多无人机任务分配[J]. 航空学报, 2020, 41(S2): 24-35. doi: 10.7527/S1000-6893.2020.24234

    WANG R R, WEI W L, YANG M C, et al. Task allocation of multiple UAVs considering cooperative route planning[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S2): 24-35(in Chinese). doi: 10.7527/S1000-6893.2020.24234
    [14]
    XU G T, LIU L, TENG L, et al. Cooperative multiple task assignment considering precedence constraints using multi-chromosome encoded genetic algorithm[C]//Proceedings of the 2018 AIAA Guidance, Navigation, and Control Conference. Reston: AIAA, 2018: 1859.
    [15]
    马也, 范文慧, 常天庆. 基于智能算法的无人集群防御作战方案优化方法[J]. 兵工学报, 2022, 43(6): 1415-1425.

    MA Y, FAN W H, CHANG T Q. Optimization method of unmanned swarm defensive combat scheme based on intelligent algorithm[J]. Acta Armamentarii, 2022, 43(6): 1415-1425(in Chinese).
    [16]
    CHEN Y B, YANG D, YU J Q. Multi-UAV task assignment with parameter and time-sensitive uncertainties using modified two-part wolf pack search algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(6): 2853-2872. doi: 10.1109/TAES.2018.2831138
    [17]
    WANG Y, YANG R R, XU Y X, et al. Research on multi-agent task optimization and scheduling based on improved ant colony algorithm[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1043(3): 032007. doi: 10.1088/1757-899X/1043/3/032007
    [18]
    SHI J Q, TAN L, LIAN X F, et al. A multi-unmanned aerial vehicle dynamic task assignment method based on bionic algorithms[J]. Computers and Electrical Engineering, 2022, 99(1): 107820.
    [19]
    ZHU Z X, TANG B W, YUAN J P. Multirobot task allocation based on an improved particle swarm optimization approach[J]. International Journal of Advanced Robotic Systems, 2017, 14(3): 1-22.
    [20]
    LI M C, LIU C B, LI K L, et al. Multi-task allocation with an optimized quantum particle swarm method[J]. Applied Soft Computing, 2020, 96(1): 106603.
    [21]
    YAN M, YUAN H M, XU J, et al. Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm[J]. EURASIP Journal on Advances in Signal Processing, 2021, 2021: 94. doi: 10.1186/s13634-021-00804-9
    [22]
    梁国强, 康宇航, 邢志川, 等. 基于离散粒子群优化的无人机协同多任务分配[J]. 计算机仿真, 2018, 35(2): 22-28. doi: 10.3969/j.issn.1006-9348.2018.02.005

    LIANG G Q, KANG Y H, XING Z C, et al. UAV cooperative multi-task assignment based on discrete particle swarm optimization algorithm[J]. Computer Simulation, 2018, 35(2): 22-28(in Chinese). doi: 10.3969/j.issn.1006-9348.2018.02.005
    [23]
    ZHANG J D, CHEN Y Y, TANG Y Q, et al. Cooperative task assignment for UAV based on SA-QCDPSO[C]//Proceedings of the 2020 IEEE 16th International Conference on Control &Automation. Piscataway: IEEE Press, 2020: 864-869.
    [24]
    何润林. 吸气式高超声速飞行器上升段轨迹优化与制导研究[D]. 北京: 清华大学, 2018: 24-27.

    HE R L. Research on trajectory optimization and guidance of air-breathing hypersonic vehicle in ascending phase[D]. Beijing: Tsinghua University, 2018: 24-27(in Chinese).
    [25]
    XUE H. A quasi-reflection based SC-PSO for ship path planning with grounding avoidance[J]. Ocean Engineering, 2022, 247(1): 110772.
    [26]
    仝秋娟, 李萌, 赵岂. 基于分类思想的改进粒子群优化算法[J]. 现代电子技术, 2019, 42(19): 11-14.

    TONG Q J, LI M, ZHAO Q. An improved particle swarm optimization algorithm based on classification[J]. Modern Electronics Technique, 2019, 42(19): 11-14(in Chinese).
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