面向超低空电磁威胁域的无人机群ELPIO协同路径规划算法

郑菊红; 宁昕; 林时尧; 刘大卫

doi:10.12382/bgxb.2025.0100

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面向超低空电磁威胁域的无人机群ELPIO协同路径规划算法

更新时间：2026-04-24

- 面向超低空电磁威胁域的无人机群ELPIO协同路径规划算法
- ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone
- 兵工学报 2026年47卷第1期页码：250100
- 作者机构：
  
  西北工业大学航天学院，陕西西安 710072
  中国兵器科学研究院，北京 100089
- 作者简介：
  
  *通信作者邮箱：kent_linsy@126.com
- 基金信息：
  
  中国科协青年人才托举工程项目(2023QNRC001)
- DOI：10.12382/bgxb.2025.0100
  中图分类号： V249.1
- 收稿：2025-02-18，
  
  网络首发：2026-02-11，
  
  纸质出版：2026-01-31
- 稿件说明：
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郑菊红, 宁昕, 林时尧, 等. 面向超低空电磁威胁域的无人机群ELPIO协同路径规划算法[J]. 兵工学报, 2026,47(1):250100.

ZHENG Juhong, NING Xin, LIN Shiyao, et al. ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone[J]. Acta Armamentarii, 2026, 47(1): 250100.
郑菊红, 宁昕, 林时尧, 等. 面向超低空电磁威胁域的无人机群ELPIO协同路径规划算法[J]. 兵工学报, 2026,47(1):250100. DOI： 10.12382/bgxb.2025.0100.

ZHENG Juhong, NING Xin, LIN Shiyao, et al. ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone[J]. Acta Armamentarii, 2026, 47(1): 250100. DOI： 10.12382/bgxb.2025.0100.

摘要

针对超低空电磁威胁域中障碍物分布密集、种类多、电磁威胁强，导致无人机群协同路径规划效率低、合理性差、易受扰等问题，提出一种改进的鸽群优化算法，提升无人机飞行的安全性及无人机群整体工作效能。分析超低空电磁威胁域的特点，并对多种类型的障碍物进行建模。在传统鸽群优化算法的不同阶段，分别引入精英学习因子和局部搜索策略，以提高算法的收敛速度和全局搜索能力。分别开展仿真实验和虚拟场景验证，并进行对比分析。研究结果表明，新算法具有较好的全局搜索能力，航路代价值更低，收敛速度更快，可为无人机群在超低空电磁威胁域内进行安全高效的路径规划提供支撑。

Abstract

The low efficiency

poor rationality and vulnerability to interference of the collaborative path planning for unmanned aerial vehicle (UAV) swarm are caused by the dense distribution diverse types of obstacles and the strong electromagnetic interference in ultra-low altitude electromagnetic threat zone. An improved pigeon-inspired optimization (PIO) algorithm is proposed to enhance the flight safety and combat effectiveness of UAVs. The characteristics of ultra-low altitude electromagnetic threat aone are analyzed

and multiple types of obstacles in ultra-low altitude electromagnetic threat zone are modeled. The elite learning factor and local search strategy are introduced in different stages of PIO algorithm to improve the convergence speed and global search ability of the improved algorithm. Simulation experiments and virtual scene experiments are conducted to verify the presented method. The results indicate that the proposed algorithm has better global search capability and faster convergence speed. It can provide support for safe and effective path planning of UAVs in ultra-low altitude electromagnetic threat zone.

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references

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