Multi-ship cooperative search method based on dynamic Voronoi partitioning

JIANG Haijun; ZHANG Yichao; SUN Yaping; CHEN Hongkun

doi:10.11993/j.issn.2096-3920.2025-0123

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

JIANG Haijun, ZHANG Yichao, SUN Yaping, CHEN Hongkun. Multi-ship cooperative search method based on dynamic Voronoi partitioning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0123

Citation:

JIANG Haijun, ZHANG Yichao, SUN Yaping, CHEN Hongkun. Multi-ship cooperative search method based on dynamic Voronoi partitioning[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0123

Citation:

PDF( 1696 KB)

Multi-ship cooperative search method based on dynamic Voronoi partitioning

doi: 10.11993/j.issn.2096-3920.2025-0123

1.
92899^thUnit, The People’s Liberation Army of China, Ningbo 315200, China
2.
92192^th Unit, The People’s Liberation Army of China, Ningbo 315200, China

Received Date: 2025-09-09
Accepted Date: 2025-10-09
Rev Recd Date: 2025-10-05

Available Online: 2026-01-15

Abstract

Abstract

Traditional multi-ship cooperative search often uses fixed partitioning and ignores target evasion, leading to low detection probability and poor realism. This paper proposes a dynamic Voronoi-based method with multi-source information fusion. Built on a Bayesian framework incorporating sonar detection and target diffusion models, it dynamically updates the probability distribution of the target’s location. Adaptive Voronoi partitioning enables distributed task allocation, reducing redundant coverage and eliminating blind spots. A multi-source scoring model integrating presence probability, unexplored area, and local entropy, with time-varying weights, balances exploration and exploitation throughout the search. Compared with fixed-area sweep and particle-swarm-based methods in 1 000 Monte Carlo simulations under evasion scenarios, the proposed method significantly reduces target acquisition time and improves detection probability, demonstrating superior realism and scalability in adversarial environments.
- multi-ship cooperation,
- search,
- Voronoi partitioning,
- information entropy,
- Bayesian estimation

FullText(HTML)

References(20)

References

[1]	王磊, 吴福初. 基于线列阵声纳的水面舰艇检查搜潜仿真[J]. 火力与指挥控制, 2011, 36(11): 91-95. WANG L, WU F C. Discussion about the efficiency of surface ships checking antisubmarine which using sonar buoy[J]. Fire Control & Command Control, 2011, 36(11): 91-95.
[2]	高永琪, 马威强, 张林森, 等. 分布式AUV协同搜索方法[J]. 系统工程与电子技术, 2022, 44(5): 1674-1675. GAO Y Q, MA W Q, ZHANG L S, et al. Distributed multi-AUVs cooperative search method[J]. Systems Engineering and Electronics, 2022, 44(5): 1674-1675.
[3]	冯西安, 寇思玮, 岳玲. 水下移动平台网络化协同探测技术发展[J]. 应用声学, 2019, 38(4): 1674-1675. doi: 10.11684/j.issn.1000-310X.2019.04.006 FENG X A, KOU S W, YUE L. Development of networked collaborative detection technology for underwater mobile platforms[J]. Journal of Applied Acoustics, 2019, 38(4): 1674-1675. doi: 10.11684/j.issn.1000-310X.2019.04.006
[4]	付乐乐, 陈宏, 巩伟杰. 基于改进蚁群算法的水下机器人路径规划[J]. 自动化与仪表, 2022, 37(4): 46-50. doi: 10.3969/j.issn.1009-0134.2022.12.039 FU L L, CHEN H, GONG W J. Path planning of underwater robots based on improved ant colony algorithm[J]. Automation & Instrumentation, 2022, 37(4): 46-50. doi: 10.3969/j.issn.1009-0134.2022.12.039
[5]	邹佳运, 曲泓玥, 陈志鹏. 大规模水下滑翔机集群区域覆盖探测路径规划[J]. 水下无人系统学报, 2021, 29(1): 23-29. doi: 10.11993/j.issn.2096-3920.2021.01.004 ZOU J Y, QU H Y, CHEN Z P. Path planning of a large-scale underwater glider swarm area coverage detection[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 23-29. doi: 10.11993/j.issn.2096-3920.2021.01.004
[6]	王飞, 陈芳香. 基于多目标粒子群算法的水面舰艇编队搜潜阵型参数优化方法[J]. 声学与电子工程, 2025, 158(2): 7-11. WANG F, CHEN F X. Optimization method for anti-submarine formation parameters of surface ship formation based on multi-objective particle swarm algorithm[J]. Acoustics and Electronics Engineering, 2025, 158(2): 7-11.
[7]	JIA Q, XU H. Research on cooperative area search of multiple underwater robots based on the prediction of initial target information[J]. Ocean Engineering, 2019, 172: 660-670. doi: 10.1016/j.oceaneng.2018.12.035
[8]	廖燕荣, 姜可宇. 多舰协同分区搜潜动态航路规划[J]. 舰船电子工程, 2022, 42(7): 23-27. doi: 10.3969/j.issn.1672-9730.2022.07.007 LIAO Y R, JIANG K Y. Dynamic route planning of partitioned searching of multiple surface ships[J]. Ship Electronic Engineering, 2022, 42(7): 23-27. doi: 10.3969/j.issn.1672-9730.2022.07.007
[9]	陈捷, 刘海颖, 李志豪, 等. 基于改进Voronoi图的集群无人机区域覆盖方法[J]. 电子设计工程, 2023, 31(6): 31-32. CHEN J, LIU H Y, LI Z H, et al. Multi-UAV area coverage method based on improved Voronoi diagram[J]. Electronic Design Engineering, 2023, 31(6): 31-32.
[10]	高永琪, 王鹏, 马威强, 等. 改进头脑风暴算法在多AUV协同搜索动态目标中的应用[J]. 国防科技大学学报, 2024, 46(6): 203-209. GAO Y Q, WANG P, MA W Q, et al. Application of improved brain storm optimization algorithm in multi-AUV cooperative search moving targets[J]. Journal of National University of Defense Technology, 2024, 46(6): 203-209.
[11]	黄杰, 孙伟, 高渝. 双属性概率图优化的无人机集群协同目标搜索[J]. 系统工程与电子技术, 2020, 42(1): 118-127. doi: 10.3969/j.issn.1001‐506X.2020.01.16 HUANG J, SUN W, GAO Y. Cooperative searching for the multi-UAVs based on dual-attribute probabilistic model optimization[J]. Systems Engineering and Electronics, 2020, 42(1): 118-127. doi: 10.3969/j.issn.1001‐506X.2020.01.16
[12]	张哲璇, 龙腾, 徐广通, 等. 重访机制驱动的多无人机协同动目标搜索方法[J]. 航空学报, 2020, 41(5): 220-232. ZHANG Z X, LONG T, XU G T, et al. Revisit mechanism driven multi-UAV cooperative search planning method for moving target[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5): 220-232.
[13]	MISHRA M, AN W et al. Context-aware decision support for antisubmarine warfare mission planning within a dynamic environment[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50(1): 318-335.
[14]	WANG C Q, MEI D, WANG Y, et al. Task allocation for Multi-AUV system: A review[J]. Ocean Engineering, 2022, 266: 112-119.
[15]	张宁, 寇小明, 李斌, 等. 基于遗传算法的应召搜潜路径优化[J]. 水下无人系统学报, 2023, 31(2): 244-251. doi: 10.11993/j.issn.2096-3920.2022-0002 ZHANG N, KOU X M, LI B, et al. Route optimization of on call submarine search based on genetic algorithm[J]. Journal of Unmanned Undersea Systems, 2023, 31(2): 244-251. doi: 10.11993/j.issn.2096-3920.2022-0002
[16]	赵苗, 高永琪, 吴笛霄, 等. 复杂海战场环境下AUV全局路径规划方法[J]. 国防科技大学学报, 2021, 43(1): 41-48. doi: 10.11887/j.cn.202101006 ZHAO M, GAO Y Q, WU D X, et al. AUV global path planning method in complex sea battle field environment[J]. Journal of National University of Defense Technology, 2021, 43(1): 41-48. doi: 10.11887/j.cn.202101006
[17]	张美燕, 蔡文郁. 基于多AUV间任务协作的水下多目标探测路径规划[J]. 传感技术学报, 2018, 31(7): 1102-1106. ZHANG M Y, CAI W Y. Underwater targets tracking path planning based on task cooperation of multiple AUVs[J]. Chinese Journal of Sensors and Actuators, 2018, 31(7): 1101-1107.
[18]	冯豪博, 胡桥, 赵振轶. 基于精英族系遗传算法的AUV 集群路径规划[J]. 系统工程与电子技术, 2022, 44(7): 2251-62. FENG H B, HU Q, ZHAO Z Y. AUV swarms path planning based on elite family genetic algorithm[J]. Systems Engineering and Electronics, 2022, 44(7): 2251-2262.
[19]	彭辉, 沈林成, 霍霄华. 多UAV协同区域覆盖搜索研究[J]. 系统仿真学报, 2007, 19(11): 2472-2476. doi: 10.3969/j.issn.1004-731X.2007.11.022 PENG H, SHEN L C, HUO X H. Research on multiple UAV cooperative area coverage searching[J]. Journal of System Simulation, 2007, 19(11): 2472-2476. doi: 10.3969/j.issn.1004-731X.2007.11.022
[20]	WAITE A D. 实用声纳工程[M]. 王德石, 译. 3版. 北京: 电子工业出版社, 2004: 155-156.