A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System

MA Yuan; CHEN Guifen; WANG Yijun; LIU Dakun; ZHANG Jun; CHEN Minghui; ZHANG Ziyang

doi:10.12382/bgxb.2025.0713

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A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System

更新时间：2026-05-06

- A Target-driven Coverage Strategy Based on Improved Seagull Optimization Algorithm for Air-to-ground Autonomous Unmanned System
- Acta Armamentarii Vol. 47, Issue 4, Pages: 250713(2026)
- 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：
- 基金信息：
- DOI：10.12382/bgxb.2025.0713
  CLC： V279;TN92
- Received：05 August 2025，
  
  Online First：27 April 2026，
  
  Published：2026-04
- 稿件说明：
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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：

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

Keywords

references

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