以移动平台与无人水下航行器（UUV）协同作业场景为研究对象，提出基于区域分割和空间缩减的UUV集群任务分配方法。首先，构建了面向移动平台与UUVs的总任务成本的任务分配框架。一方面，通过区域分割方法实现了任务点的任务初分配，提高了初始解的质量。此外，基于邻解生成的3种方法能够更高效地探索解空间，实现样本解的多样性。另一方面，通过基于空间缩减的蚁群优化算法实现了UUVs的任务时序优化，加速优化收敛。此外，采用UUVs路径终点与移动平台的最短距离判定，确定了移动平台与UUVs之间的回收时机。最后，通过算例分析，所提方法在与其他经典算法的实验数据对比中，其任务成本更低，验证了基于区域分割和空间缩减的UUV集群任务分配方法的有效性。
Taking the collaborative operation scenario between mobile platforms and Unmanned Underwater Vehicle (UUV) as the research object, this paper proposes UUV cluster task allocation method based on regional segmentation and space reduction. Firstly, a task allocation framework oriented to the total task cost of mobile platforms and UUVs is constructed. On the one hand, the initial task allocation of task points is realized through the region division method, which improves the quality of the initial solution. In addition, three methods based on neighboring solution generation can explore the solution space more efficiently and achieve the diversity of sample solutions. On the other hand, the task sequence optimization of UUVs is realized through the ant colony optimization algorithm based on space reduction, which accelerates the optimization convergence. Furthermore, the shortest distance determination between the end point of the UUVs’ path and the mobile platform is adopted to determine the recovery timing between the mobile platform and the UUVs. Finally, through case analysis, the proposed method has lower task cost in the comparison of experimental data with other classical algorithms, which verifies the effectiveness of the UUV cluster task allocation method based on region division and space reduction.
2026,48(6): 199-205 收稿日期：2025-10-17
DOI：10.3404/j.issn.1672-7649.2026.06.026
分类号：U674.941；TP242.6
基金项目：军工科研院所稳定支持项目（WDZC-2-05）
作者简介：李天博(1993-),男,博士,助理研究员,研究方向为水下航行器总体设计、集群规划
参考文献：
[1] 秦洪德, 孙延超. AUV关键技术与发展趋势[J]. 舰船科学技术, 2020, 42(23): 25-28
QIN H D, SUN Y C. Key technologies and development trends of AUV[J]. Ship Science and Technology, 2020, 42(23): 25-28
[2] 邢铭涵, 商志刚, 乔钢, 等. 基于最优通信链路选择的跨域通信浮标系统[J]. 水下无人系统学报, 2024, 32(4): 650-658
XING M H, SHANG Z G, QIAO G, et al. Cross-domain communication buoy system based on optimal communication link selection[J]. Journal of Unmanned Undersea Systems, 2024, 32(4): 650-658
[3] 邱志明, 马焱, 孟祥尧, 等. 水下无人装备前沿发展趋势与关键技术分析[J]. 水下无人系统学报, 2023, 31(1): 1-9
QIU Z M, MA Y, MENG X R, et al. Analysis of frontier development trends and key technologies for unmanned undersea equipment[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 1-9
[4] 杨洋, 王征, 胡致远, 等. 无人水下航行器编队控制研究现状及技术综述[J]. 舰船电子工程, 2022, 42(2): 1-7
YANG Y, WANG Z, HU Z Y, et al. Review of research status and technologies of unmanned underwater vehicle formation control[J]. Ship Electronic Engineering, 2022, 42(2): 1-7
[5] ZHANG L L, WU S J, TANG C K. UUV cluster navigation data extraction fusion positioning algorithm with information geometry[J]. Ocean Engineering, 2024, 314(1): 119646
[6] WANG S, LIU Y, QIU Y, et al. An efficient distributed mission allocation method for maximizing mission allocations of multirobot systems[J]. IEEE Transactions on Automation Science and Engineering. 2024, 21(3): 3588-3602.
[7] 张瑞鹏, 冯彦翔, 杨宜康. 多无人机协同任务分配混合粒子群算法[J]. 航空学报, 2022, 43(12): 167-173
ZHANG R P, FENG Y X, YANG Y K. Hybrid particle swarm optimization for multi-UAV cooperative task assignment[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(12): 167-173
[8] GNANA SAMBANDAM K, ETHALA K. A hybrid ant colony tabu search algorithm for solving mission assignment problem in heterogeneous processors[J]. Advances in Intelligent Systems and Computing, 2016, 398: 319-329
[9] 宋育武, 贾林通, 李娟, 等. 异构型无人机群体并行任务分配算法[J]. 科学技术与工程, 2020, 20(4): 1492-1497
SONG Y W, JIA L T, LI J, et al. Parallel task assignment algorithm for heterogeneous UAV swarms[J]. Science Technology and Engineering, 2020, 20(4): 1492-1497
[10] LI J, ZHANG K, XIA G. Multi-AUV cooperative mission allocation based on improved contract network[C]//2017 IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan, 2017.
[11] LI J, ZHANG J X, ZHANG G S, et al. An adaptive prediction target search algorithm for multi-AUVs in an unknown 3d environment[J]. Sensors, 2018, 18(11).
[12] MAHMOUDZADEH S, POWERS D M W, SAMMUT K, et al. A hierarchal planning framework for AUV mission management in a spatiotemporal varying ocean[J]. Computers & Electrical Engineering, 2018: 741-760.
[13] ZHAO X, WANG Y, LIU J, et al. Mission planning algorithm of multi-AUV based on energy constraint[J]. Journal of Computer Applications, 2019, 39(9): 2529-2534
[14] 王童豪, 彭星光, 胡浩, 等. 海上有人/无人协同系统及其关键技术综述[J]. 兵工学报, 2024, 45(10): 3317-3340
WANG T H, PENG X G, HU H, et al. Review of maritime manned/unmanned collaborative systems and their key technologies[J]. Acta Armamentarii, 2024, 45(10): 3317-3340
