Distributed Cooperative Search Method for Multi-UAV in Unknown Environment

JIANG Haoxian; ZHOU Rui

doi:10.12382/bgxb.2025.0249

您当前的位置：

首页 >

文章列表页 >

Distributed Cooperative Search Method for Multi-UAV in Unknown Environment

更新时间：2026-04-24

- Distributed Cooperative Search Method for Multi-UAV in Unknown Environment
- Acta Armamentarii Vol. 47, Issue 3, Pages: 250249(2026)
- 作者机构：
  
  北京航空航天大学自动化科学与电气工程学院，北京 100191
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0249
  CLC： TP273
- Received：03 April 2025，
  
  Online First：05 February 2026，
  
  Published：2026-03
- 稿件说明：
移动端阅览
JIANG Haoxian, ZHOU Rui. Distributed Cooperative Search Method for Multi-UAV in Unknown Environment[J]. Acta Armamentarii, 2026, 47(3): 250249.
DOI：

JIANG Haoxian, ZHOU Rui. Distributed Cooperative Search Method for Multi-UAV in Unknown Environment[J]. Acta Armamentarii, 2026, 47(3): 250249. DOI： 10.12382/bgxb.2025.0249.

摘要

为快速降低环境不确定性并减少搜索重叠，从而提高协同搜索效率，提出一种适用于未知环境且通信受限条件下的多无人机分布式协同搜索方法。针对协同区域搜索问题，构建一种基于迭代加权Voronoi图划分的分布式区域划分算法，实现任务区域的动态均衡划分。设计一种基于动态目标选择和参考路径生成的策略，以引导无人机优化搜索过程。采用基于粒子群优化的分布式模型预测控制算法，构建多目标代价函数以优化控制指令，从而避免陷入局部最优并保障无人机的安全性。通过仿真实验，将所提算法与分布式反聚集算法进行对比。研究结果表明：所提算法能够更快实现区域覆盖，并通过增大无人机之间的平均间距有效降低搜索重叠，从而提升系统整体搜索效率与飞行安全性；在障碍环境、不同参数设置及通信受限条件下的测试结果进一步表明，该算法在满足无人机安全约束的前提下能够保持稳定的搜索性能，搜索效率随参数变化的波动较小，表现出良好的实时性与鲁棒性。

Abstract

In order to quickly reduce environmental uncertainty and search overlap

thereby improving the efficiency of collaborative search

a distributed collaborative search method for multiple unmanned aerial vehicles（UAVs）is proposed

which is suitable for unknown environments under communication-constrained conditions. For the collaborative area search problem

a distributed region partitioning algorithm based on iterative weighted Voronoi diagram partitioning is proposed to achieve the dynamic and balanced partitioning of task area. A strategy based on dynamic target selection and reference path generation method is designed to guide UAVs to optimize the search process. Then

a distributed model predictive control algorithm based on particle swarm optimization is employed to construct a multi-objective cost function for optimizing control commands

thereby avoiding being trapped in local optima and ensuring the safety of UAV. The proposed algorithm is compared with the distributed anti-aggregation algorithm through simulation experiments. The results show that the proposed algorithm can achieve faster area coverage and effectively reduce search overlap by increasing the average inter-UAV distance

thus improving overall search efficiency and flight safety. Further tests under obstacle environments

different parameter settings and limited communication conditions demonstrate that the proposed algorithm maintains stable search performance while satisfying the safety constraints of UAVs

with small fluctuation of search efficiency being with the change in parameter

exhibiting good real-time capability and robustness.

关键词

Keywords

references

DONG J, OTA K, DONG M X. UAV-based real-time survivor detection system in post-disaster search and rescue operations[J]. IEEE Journal on Miniaturization for Air and Space Systems,2021, 2(4):209-219.

ASADZADEH S, de OLIVEIRA W J, de SOUZA FILHO C R. UAV-based remote sensing for the petroleum industry and environmental monitoring: state-of-the-art and perspectives[J]. Journal of Petroleum Science and Engineering,2022,208:109633.

LIU D Q, BAO W D, ZHU X M, et al. Cooperative path optimization for multiple UAVs surveillance in uncertain environment[J]. IEEE Internet of Things Journal,2021,9(13):10676-10692.

CHANDRAN I, VIPIN K. Multi-UAV networks for disaster monitoring:challenges and opportunities from a network perspective[J]. Drone Systems and Applications,2024,12:1-28.

李军,陈士超.无人机蜂群关键技术发展综述[J].兵工学报, 2023,44(9):2533-2545.

LI J,CHEN S C. Overview of key technology and its development of drone swarm[J]. Acta Armamentarii,2023,44(9):2533-2545. (in Chinese)

SONG Z Y,QIN X,HAO Y J,et al. A comprehensive survey on aerial mobile edge computing: challenges, state-of-the-art, and future directions[J]. Computer Communications,2022,191:233-256.

于驷男,周锐,夏洁,等.多无人机协同搜索区域分割与覆盖[J].北京航空航天大学学报,2015,41(1):167-173.

YU S N,ZHOU R,XIA J,et al. Decomposition and coverage of multi-UAV cooperative search area[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 167-173. (in Chinese)

戴健,许菲,陈琪锋.多无人机协同搜索区域划分与路径规划[J].航空学报,2020,41(增刊1):149-156.

DAI J,XU F,CHEN Q F. Multi-UAV cooperative search on region division and path planning[J]. Acta Aeronautica et Astronautica Sinica,2020,41(S1):149-156. (in Chinese)

GANGANATH N,TONY C C, TSE C K. Distributed antiflocking algorithms for dynamic coverage of mobile sensor networks[J]. IEEE Transactions on Industrial Informatics,2016,12(5):1795-1805.

GANGANATH N, YUAN W M, CHENG C T, et al. Territorial marking for improved area coverage in anti-flocking-controlled mobile sensor networks[C]∥Proceedings of 2018 IEEE International Symposium on Circuits and Systems . Florence, Italy:IEEE,2018:1-4.

尹洪玉,吴宇,梁天骄.固定翼无人机巡逻覆盖协同路径规划方法[J].航空学报,2024,45(6):329-343.

YIN H Y,WU Y,LIANG T J. Cooperative path planning for patrol coverage of fixed wing UAV[J]. Acta Aeronautica et Astronautica Sinica,2024,45(6):328944. (in Chinese)

符小卫,魏广伟,高晓光.不确定环境下多无人机协同区域搜索算法[J].系统工程与电子技术,2016,38(4):821-827.

FU X W,WEI G W,GAO X G. Cooperative area search algorithm for multi-UAVs in uncertainty environment[J]. Systems Engineering and Electronics, 2016, 38 (4): 821 - 827. (in Chinese)

王宁,李哲,梁晓龙,等.基于搜索意图交互的无人机集群协同搜索算法[J].北京航空航天大学学报,2022, 48 (3):454-463.

WANG N,LI Z,LIANG X L,et al. Cooperative search algorithm for UAV swarm based on search intention interaction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3):454-463. (in Chinese)

文超,董文瀚,解武杰,等.基于回访机制的无人机集群分布式协同区域搜索方法[J].航空学报,2023,44(11):253-270.

WEN C,DONG W H,XIE W J,et al. Distributed cooperative area search method for UAV swarms based on revisit mechanism[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44 (11): 253-270. (in Chinese)

YOUSSEFI K A R, ROUHANI M. Swarm intelligence based robotic search in unknown maze-like environments[J]. Expert Systems with Applications,2021,178:114907.

王茂,周少武,张红强,等.未知环境下群机器人多目标搜索协同控制[J].控制理论与应用,2022,39(4):750-760.

WANG M,ZHOU S W,ZHANG H Q,et al. Multi-target search of swarm robots cooperative control in an unknown environment[J]. Control Theory & Applications, 2022, 39 (4): 750-760. (in Chinese)

周鹤翔,徐扬,罗德林.针对动态目标的多无人机协同组合差分进化搜索方法[J].控制与决策,2023,38(11):3128-3136.

ZHOU H X,XU Y,LUO D L. A composite differential evolution algorithm for multi-UAV cooperative dynamic target search[J]. Control and Decision,2023,38(11):3128-3136. (in Chinese)

柳汀,周国鑫,徐扬,等.融合信息图的优化哈里斯鹰多无人机动态目标搜索[J].航空学报,2024,45(增刊1):346-357.

LIU T,ZHOU G X,XU Y,et al. Optimised Harris hawks multi-UAV dynamic target search with fused infographics[J]. Acta Aeronautica et Astronautica Sinica,2024,45(S1):346-357. (in Chinese)

CHAI S Y,YANG Z,HUANG J C,et al. Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios[J]. Defence Technology, 2024, 31:295-311.

XIAO J, YUAN G H, XUE Y X, et al. A deep reinforcement learning based distributed multi-UAV dynamic area coverage algorithm for complex environment[J]. Neurocomputing,2024, 595:127904.

YUAN G H,XIAO J,HE J H,et al. Multi-agent cooperative area coverage:a two-stage planning approach based on reinforcement learning[J]. Information Sciences,2024,678:121025.

XIAO J P, PISUTSIN P, FEROSKHAN M. Collaborative target search with a visual drone swarm: an adaptive curriculum embedded multistage reinforcement learning approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(1):313-327.

Views

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Distributed Guidance Law for Multi-UAV Cooperative Tracking

Distributed Cooperative Guidance of Multiple Missiles with Heterogeneous Constraints of Field-of-view

Motion Control of Wheeled Bipedal Robot Based on Double-layer MPC

Related Author

ZHOU Rui

JIANG Haoxian

WANG Zhifang

ZHU Shaohui

LIU Xinyang

LIN Min

XU Gang

LIU Yong

Related Institution

College of Control Science and Engineering, Zhejiang University

Tianjin Navigation Equipment Research Institute

Polytechnic Institute of Zhejiang University, Zhejiang University

School of Mechatronical Engineering, Beijing Institute of Technology

Unit 63961 of PLA

Postal code：100089
Tel：010-68963060/68962718 Fax：010-68963025 Email：bgxb@cos.org.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05059581号-4 京公网安备11010802024360号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：119050 Visits Today：37

⁰