ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone

ZHENG Juhong; NING Xin; LIN Shiyao; LIU Dawei

doi:10.12382/bgxb.2025.0100

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ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone

更新时间：2026-04-24

- ELPIO-based Collaboration Path Planning for UAV Swarm in Ultra-low Altitude Electromagnetic Threat Zone
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250100(2026)
- 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：
- 基金信息：
- DOI：10.12382/bgxb.2025.0100
  CLC： V249.1
- Received：18 February 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
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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：

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.

关键词

Keywords

references

朱心茹.察打一体无人机分队城市协同作战任务规划研究[D].南京：南京理工大学，2022.

ZHU X R.Research on mission planning of reconnaissance and strike integrated UAV detachments in urban cooperative operations [D].Nanjing:Nanjing University of Science & Technology,2022.（in Chinese）

胡明哲，李旭光，任智颖，等.基于改进启发函数的A * 算法的无人机三维路径规划[J ] .兵工学报，2024,45（增刊1）:302-307.

HU M Z, LI G X, REN Z Y, et al.UAV 3D path planning based on A * algorithm with improved heur istic function [J ] .Acta Armamentarii,2024,45（S1）:302-307.（in Chinese）

王东振，张岳，赵宇，等.基于RRT-Dubins的无人机航迹优化方法[J].兵工学报，2024,45（8）:2761-2773.

WANG D Z, ZHANG Y, ZHAO Y, et al.A UAV trajectory optimization method based on RRT-Dubins[J].Acta Armamentarii, 2024,45（8）:2761-2773.（in Chinese）

PACHUNG P, PANDYA K, NAGAR A, et al. Efficient 3D path planning for drone swarm using improved sine cosine algorithm [J]. SN Computer Science,2024,5（3）:286.

卢颖，庞黎晨，陈雨思，等.一种面向城市战的无人机路径规划群智能算法[J].兵工学报，2023,44（增刊2）:146-156.

LU Y, PANG L C, CHEN Y S, et al.A swarm intelligence algorithm for UAV path planning in urban warfare [J].Acta Armamentarii,2023,44（S2）:146-156.（in Chinese）

郭志明，娄文忠，李涛，等.基于改进蝗虫优化算法考虑任务威胁的多无人机协同航迹规划[J].兵工学报，2023, 44（增刊2）:52-60.

GUO Z M, LOU W Z, LI T, et al.Collaborative route planning of multiple unmanned aerial vehicles considering task threats based on improved grasshopper optimization algorithm [J].Acta Armamentarii,2023,44（S2）:52-60.（in Chinese）

PAN Z H, ZHANG C X, XIA Y Q, et al. An improved artificial potential field method for path planning and formation control of the multi-UAV systems [J]. IEEE Transactions on Circuits and Systems II:Express Briefs,2022,69（3）:1129-1133.

LIU Y, ZHANG Y, LI M Q, et al. Multi-UAV path planning based on fusion of sparrow search algorithm and improved bioinspired neural network[J]. IEEE Access,2021,9:124670-124681.

DUAN H B, LIU S Q. Non-linear dual-mode receding horizon control for multiple unmanned air vehicles formation flight based on chaotic particle swarm optimization [J]. IET Control Theory &Applications,2010,4:2565-2578.

方群，徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报，2017,35（1）:66-73.

FANG Q, XU Q.UAV 3D track planning based on improved particle swarm optimization [J].Northwestern Polytechnical University,2017,35（1）:66-73.（in Chinese）

徐瑞莲，周新志，宁芋.基于改进差分进化算法的多无人机航迹规划[J].火力与指挥控制，2020,45（1）:169-179.

XU R L, ZHOU X Z, NING Q.Multi-UAV track planning based on improved differential evolution algorithm[J].Fire Control &Command Control,2020,45（1）:169-179.（in Chinese）

CHAI X Z, ZHENG Z S, XIAO J M, et al. Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment [J]. Aerospace Science and Technology, 2022, 121:107287.

韩增亮，陈谋，朱豪杰，等.基于HME-ABC算法的多无人直升机时间协同航路规划[J].指挥与控制学报，2023,9（6）:699-708.

HAN Z L, CHEN M, ZHU H J, et al.Multi-UAV time coordinated path planning based on HME-ABC algorithm [J].Journal of Command and Control,2023,9（6）:699-708.（in Chinese）

GUO J P, CHEN R W. A path planning method for unmanned aerial vehicles based on improved artificial bee colony algorithm [C]∥Proceedings of the 7th International Conference on Electrical, Mechanical and Computer Engineering. Xi'an, China:IEEE,2023:665-669.

HUANG G W, CAI Y G, LIU J Q, et al. A novel hybrid discrete grey wolf optimizer algorithm for multi-UAV path planning[J]. Journal of Intelligent & Robotic Systems,2021,103:49.

RAJEEV K, LAXMAN S, RAJDEV T. Novel reinforcement learning guided enhanced variable weight grey wolf optimization（RLV-GWO）algorithm for multi-UAV path planning [J]. Wireless Personal Communications,2023,131:2093-2123.

DUAN H B, QIAO P X. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning [J].International Journal of Intelligent Computing and Cybernetics, 2014,7:24-37.

段海滨，邱华鑫，范彦铭.基于捕食逃逸鸽群优化的无人机紧密编队协同控制[J].中国科学：技术科学，2015,45（6）:559-572.

DUAN H B, QIU H X, FAN Y M.Unmanned aerial vehicle close formation cooperative control based on predatory escaping pigeoninspired optimization [J].Scientia Sinica Technologica, 2015, 45（6）:559-572.（in Chinese）

李亚飞，赵瑞.城市复杂环境下多目标无人机路径规划研究[J].南京航空航天大学学报，2024,56（6）:1002-1012.

LI Y F, ZHAO R.Research on multi-target UAV path planning in urban complex environments[J].Journal of Nanjing University of Aeronautics & Astronautics, 2024, 56（6）: 1002-1012.（in Chinese）

屠壮，杨柳庆，林志坚，等.城市作战背景下多无人机航路规划仿真研究[J].舰船电子工程，2024,44（9）:48-54.

TU Z, YANG L Q, LIN Z J, et al.Simulation research on multi-UAV route planning under the background of urban combat[J].Ship Electronic Engineering,2024,44（9）:48-54.（in Chinese）

CHEN Y B, YU J Q, MEI Y S, et al. Modified central force optimization（MCFO）algorithm for 3D UAV path planning[J]. Neurocomputing,2016,171:878-888.

卢颖，庞黎晨，陈雨思，等.一种面向城市战的无人机路径规划群智能算法[J].兵工学报，2023,44（增刊2）:146-156.

LU Y, PANG L C, CHEN Y S, et al.A swarm intelligence algorithm for UAV path planning in urban warfare [J].Acta Armamentarii,2024,44（S2）:146-156.（in Chinese）

GUIFORD T, ROBERTS S, BIRO D, et al. Positional entropy during pigeon homing II:navigational interpretation of Bayesian latent state models [J]. Journal of Theoretical Biology, 2004, 227:25-38.

段海滨，杨之元.基于柯西变异鸽群优化的大型民用飞机滚动时域控制[J].中国科学：技术科学，2018,48（3）:277-288.

DUAN H B, YANG Z Y.Large civil aircraft receding horizon control based on Cauthy mutation pigeon inspired optimization [J].Scientia Sinica Technologica,2018,48（3）:277-288.（in Chinese）

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