基于改进海鸥优化算法的空地自主无人系统目标驱动覆盖策略

马元; 陈桂芬; 王义君; 刘大鹍; 张军; 陈明辉; 张子阳

doi:10.12382/bgxb.2025.0713

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基于改进海鸥优化算法的空地自主无人系统目标驱动覆盖策略

更新时间：2026-05-06

- 基于改进海鸥优化算法的空地自主无人系统目标驱动覆盖策略
- A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System
- 兵工学报 2026年47卷第4期页码：250713
- 作者机构：
  
  长春理工大学电子信息工程学院，吉林长春 130022
  北斗应用发展研究院，北京 100089
- 作者简介：
  
  通信作者邮箱：chenguif@163.com
- 基金信息：
  
  国家自然科学基金项目(61540022)
- DOI：10.12382/bgxb.2025.0713
  中图分类号： V279;TN92
- 收稿：2025-08-05，
  
  网络首发：2026-04-27，
  
  纸质出版：2026-04
- 稿件说明：
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马元, 陈桂芬, 王义君, 等. 基于改进海鸥优化算法的空地自主无人系统目标驱动覆盖策略[J]. 兵工学报, 2026,47(4):250713.

MA Yuan, CHEN Guifen, WANG Yijun, et al. A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System[J]. Acta Armamentarii, 2026, 47(4): 250713.
马元, 陈桂芬, 王义君, 等. 基于改进海鸥优化算法的空地自主无人系统目标驱动覆盖策略[J]. 兵工学报, 2026,47(4):250713. DOI： 10.12382/bgxb.2025.0713.

MA Yuan, CHEN Guifen, WANG Yijun, et al. A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System[J]. Acta Armamentarii, 2026, 47(4): 250713. DOI： 10.12382/bgxb.2025.0713.

摘要

空地自主无人系统区域覆盖过程中存在全局耦合易陷入局部最优，以及覆盖冗余度高等问题，针对这些问题提出一种目标驱动覆盖策略（Target Driven Coverage method for air to ground autonomous unmanned system based on Seagull Optimization，TDCSO）。该策略构建了包含避免碰撞、动态转移与位置优化3个层次的区域覆盖模型，并设计改进海鸥优化算法（Improved Seagull Optimization Algorithm，ISOA）作为核心引擎。ISOA在海鸥优化算法基础上，引入柯西变异用以辅助降低自主无人系统之间的相互干扰，并定义适应权重系数，通过调整权重系数数值大小控制寻优点位置信息，从而引导自主无人系统转移方向，利用非线性动态自适应步长因子改变搜索距离，扩大搜索范围。仿真结果表明，所提策略得到的区域覆盖计算结果与平衡优化器算法、麻雀搜索算法、鲸鱼优化算法和灰狼优化算法等主流覆盖算法结果相比，收敛速度至少提高7%，同时有效降低了覆盖冗余度，覆盖率可达95.16%，平均连通率至少提高2.35%。

Abstract

To address the problems of global coupling easily leading to local optima and high coverage redundancy in the area coverage process of air-to-ground autonomous unmanned systems

a target-driven coverage strategy based on seagull optimization (TDCSO) is proposed for air-to-ground autonomous unmanned systems. This strategy constructs an area coverage model comprising of three layers:collision avoidance

dynamic transition and position optimization

and an improved seagull optimization algorithm (ISOA) as a core optimization engine of the strategy is designed. On the basis of the seagull optimization algorithm

the ISOA introduces Cauchy mutation to assist in minimizing the mutual interference among autonomous unmanned systems. An adaptive weight coefficient is defined to control the position information of optimizing points by adjusting its value so as to guide the transition direction of autonomous unmanned systems

and a nonlinear dynamic adaptive step size factor is used to alter the search distance and expand the search scope. Simulated results demonstrate that

compared with the mainstream coverage algorithms including the equilibrium optimizer algorithm

sparrow search algorithm

whale optimization algorithm and gray wolf optimization algorithm

the proposed strategy improves the convergence speed by at least 7%

effectively reduces the coverage redundancy with a coverage rate of up to 95.16%

and raises the average connectivity rate by at least 2.35%.

关键词

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references

刘大鹍，陈桂芬，王义君.自组织网络区域覆盖协作控制算法[J].兵工学报，2020，41（6）：1131-1139.

LIU D K, CHEN G F, WANG Y J.Regional coverage cooperative control algorithm for Ad Hoc networks [J]. Acta Armamentarii, 2020, 41 (6): 1131-1139. (in Chinese)

ZHAO X R, YANG R N, ZHANG Y, et al. Deep reinforcement learning for intelligent dual-UAV reconnaissance mission planning [J]. Electronics, 2022, 11 (13): 2031.

王义君，陈忠野，缪瑞新，等.基于三角剖分的WSNs元胞感知覆盖算法[J].电子学报，2022，50（10）：2443-2451.

WANG Y J, CHEN Z Y, MIAO R X, et al. A cellular perceptron coverage algorithm based on triangulation in WSNs [J]. Acta Electronica Sinica, 2022, 50 (10): 2443-2451. (in Chinese)

ZENG C J, QIN T, TAN W, et al. Coverage optimization of heterogeneous wireless sensor network based on improved wild horse optimizer [J]. Biomimetics, 2023, 8 (1): 70.

WANG J B, WANG R X. Multi-UAV area coverage track planning based on the voronoi graph and attention mechanism [J]. Applied Sciences, 2024, 14 (17): 7844.

GÜVEN I, YANMAZ E. Multi-objective path planning for multi-UAV connectivity and area coverage [J]. Ad Hoc Networks, 2024, 160:103520.

SAVKIN A V, HUANG H L. A method for optimized deployment of a network of surveillance aerial drones [J]. IEEE Systems Journal, 2019, 13 (4): 4474-4477.

MA D Z, LI Y B, HU X G, et al. An optimal three-dimensional drone layout method for maximum signal coverage and minimum interference in complex pipeline networks [J]. IEEE Transactions on Cybernetics, 2021, 52 (7): 5897-5907.

TAREKEGN G B, JUANG R T, LIN H P, et al. Deepreinforcement-learning-based drone base station deployment for wireless communication services [J]. IEEE Internet of Things Journal, 2022, 9 (21): 21899-21915.

ZHANG X, XIANG X, LU S S, et al. Evolutionary optimization of drone-swarm deployment for wireless coverage [J]. Drones, 2023, 7 (1): 8.

陈进朝，王洋，张营，等.基于时空密度聚类的异构无人机集群覆盖路径规划方法[J].电子学报，2025，53（3）：705-715.

CHEN J C, WANG Y, ZHANG Y, et al. Coverage path planning for heterogeneous UAVs based on temporal-spatial density clustering [J]. Acta Electronica Sinica, 2025, 53 (3): 705-715. (in Chinese)

田双喜，陈洪辉，徐彬杰，等.车辆支持的多无人机多区域覆盖路径规划算法[J].国防科技大学学报，2024，46（6）：227-234.

TIAN S X, CHEN H H, XU B J, et al. Coverage path planning algorithm for multi-area by truck-supported multi-UAV [J]. Journal of National University of Defense Technology, 2024, 46 (6): 227-234. (in Chinese)

YAO Y D, LIAO H M, LIU M, et al. Coverage optimization strategy for 3-D wireless sensor networks based on improved sparrow search algorithm [J]. IEEE Sensors Journal, 2023, 23 (19): 23721-23733.

CHEN J C, DU C L, ZHANG Y, et al. A clustering-based coverage path planning method for autonomous heteroge- neous UAVs [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (12): 25546-25556.

YU H L, MEIER K, ARGYLE M, et al. Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles [J]. IEEE/ASME Transactions on Mechatronics, 2015, 20 (2): 541-552.

JAVADI S H, MOHAMMADI A, FARINA A. Serial Plackett fusion for decision making [J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56 (1): 811-816.

TANG G, TANG C Q, ZHOU H, et al. R-DFS: a coverage path planning approach based on region optimal decomposition [J]. Remote Sensing, 2021, 13 (8): 1525.

FARAMARZI A, HEIDARINEJAD M, STEPHENS B, et al. Equilibrium optimizer: a novel optimization algorithm [J]. Knowledge-Based Systems, 2020, 191:105190.

YANG X T, WEN Y Y, YUAN D N, et al. Coverage degreecoverage model in wireless visual sensor networks [J]. IEEE Wireless Communications Letters, 2019, 8 (3): 817-820.

TOLOUEIASHTIAN M, GOLSORKHTABARAMIRI M, RAD S Y B. Solving point coverage problem in wireless sensor networks using whale optimization algorithm [J]. The International Arab Journal of Information Technology, 2021, 18 (6): 830-838.

LI K W, LI S H, HUANG Z C, et al. Grey wolf optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy [J].Scientific Reports, 2022, 12 (1): 18961.

钱志鸿，王义君.低空经济赋能者：智能无人机技术体系综述与展望[J].电子与信息学报，2026，48（1）：1-33.

QIAN Z H, WANG Y J.Intelligent unmanned aerial vehicles for low-altitude economy: a review of the technology framework and future prospects [J]. Journal of Electronics & Information Technology, 2026, 48 (1): 1-33. (in Chinese)

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