Grey-wolf-inspired Cooperative Decision-making for Unmanned Clusters in Degraded Communication Environments: Dynamic Task Scheduling and Communication-aware Route Optimization

Xuewei YU; Weilong SONG; Yong LIU; Miaohan ZHANG; Zhi LI; Jianlei ZHANG

doi:10.12382/bgxb.2025.0986

您当前的位置：

首页 >

文章列表页 >

Grey-wolf-inspired Cooperative Decision-making for Unmanned Clusters in Degraded Communication Environments: Dynamic Task Scheduling and Communication-aware Route Optimization

更新时间：2026-02-10

- Grey-wolf-inspired Cooperative Decision-making for Unmanned Clusters in Degraded Communication Environments: Dynamic Task Scheduling and Communication-aware Route Optimization
- Acta Armamentarii Vol. 46, Issue S2, Pages: 250986(2025)
- 作者机构：
  
  1. 南开大学人工智能学院, 天津 300071
  2. 中兵智能创新研究院有限公司, 北京 100071
  3. 群体协同与自主实验室, 100071
  4. 中国北方车辆研究所, 北京 100072
  5. 西北机电工程研究所, 咸阳 712099
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0986
  CLC：
- Received：04 November 2025，
  
  Online First：03 February 2026，
  
  Published：2025
- 稿件说明：
移动端阅览
Xuewei YU, Weilong SONG, Yong LIU, et al. Grey-wolf-inspired Cooperative Decision-making for Unmanned Clusters in Degraded Communication Environments: Dynamic Task Scheduling and Communication-aware Route Optimization[J]. Acta Armamentarii, 2025, 46(S2): 250986.
DOI：

Xuewei YU, Weilong SONG, Yong LIU, et al. Grey-wolf-inspired Cooperative Decision-making for Unmanned Clusters in Degraded Communication Environments: Dynamic Task Scheduling and Communication-aware Route Optimization[J]. Acta Armamentarii, 2025, 46(S2): 250986. DOI： 10.12382/bgxb.2025.0986.

摘要

针对恶劣通信环境下无人集群协同决策鲁棒性不足、效率受限的问题

提出一种仿灰狼的分级领导驱动一体化协同决策方法

将任务动态调度与通信感知路径规划进行联合优化。在任务调度层

借鉴灰狼社会层级构建

三级动态指派机制

并设计时间-事件双触发的任务重分配策略;在路径规划层

构建基于状态机的算法

通过自适应权重在路径效率、通信保障和生存安全之间实现动态平衡。该一体化机制使集群在链路高丢包、时变衰落、通信距离受限以及存在复杂障碍的复合约束下

仍能对侦察、打击、评估等多阶段任务实现自适应且鲁棒的协同决策。基于机器人操作系统的仿真结果表明

在10~30个目标规模下

该方法能将任务完成率稳定保持在100%

平均完成时间相比基线方案最多降低65%

验证了其对通信波动的抑制作用和对任务闭环的保障能力

可为复杂受限环境下无人集群协同决策的工程应用提供方法支撑。

Abstract

The cooperative decision-making of unmanned swarm lacks robustness and efficiency in the degraded communication environments.A grey wolf-inspired decision-making method is proposed

which utilizes a hierarchical leadership-driven framework for joint dynamic task scheduling and communication-aware path planning.An

hierarchy for dynamic assignment is established and a time-event dual-trigger strategy is designed in the task scheduling layer.In the path planning layer

a state-machine-based algorithm is constructed to achieve a dynamic balance among path efficiency

communication assurance and survivability by using the adaptive weights.This integrated mechanism enables unmanned swarm to make the adaptive and robust collaborative decisions for the multi-stage mission (reconnaissance

strike and assessment) under composite constraints

including high packet loss

time-varying fading

limited range

and complex obstacles.The simulated results based on the robot operating system (ROS) demonstrate that this proposed method can be used to maintain a sustained 100% completion rate and reduce the average task completion time by up to 65% compared to the baseline schemes in the case of 10~30 targets.These results validate that the proposed method has the ability to suppress communication fluctuations and ensure the mission closure

supporting the engineering applications of unmanned swa

rm cooperative decision-making in complex constrained environments.

关键词

Keywords

references

BI W H , ZHANG M Q , CHEN H , et al. Cooperative task allocation method for air-sea heterogeneous unmanned system with an application to ocean environment information monitoring [J ] . Ocean Engineering , 2024 , 309 ( 2 ): 118496 .

向锦武 , 董希旺 , 丁文锐 , 等 . 复杂环境下无人集群系统自主协同关键技术 [J ] . 航空学报 , 2022 , 43 ( 10 ): 325 - 357 .

XIANG J W , DONG X W , DING W R , et al. Key technologies for autonomous cooperation of unmanned swarm systems in complex environments [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 10 ): 325 - 357 . (in Chinese)

潘振华 , 夏元清 , 鲍泓 , 等 . 无人智能集群系统决策与控制研究进展 [J ] . 中国图象图形学报 , 2024 , 29 ( 11 ): 3195 - 3215 .

PAN Z H , XIA Y Q , BAO H , et al. Research progress in decision-making for unmanned intelligent swarm system and control [J ] . Journal of Image and Graphics , 2024 , 29 ( 11 ): 3195 - 3215 . (in Chinese)

MOU Z Y , GAO F F , LIU J , et al. Resilient UAV swarm communications with graph convolutional neural network [J ] . IEEE Journal on Selected Areas in Communications , 2022 , 40 ( 1 ): 393 - 411 .

张子龙 , 徐胜 , 樊县林 . 新型作战概念下的军用通信系统发展趋势 [J ] . 指挥信息系统与技术 , 2023 , 14 ( 3 ): 51 - 56 .

ZHANG Z L , XU S , FAN X L . Development trends of military communication systems under new operational concepts [J ] . Command Information System and Technology , 2023 , 14 ( 3 ): 51 - 56 . (in Chinese)

穆巍炜 , 何滨兵 , 齐尧 , 等 . 基于环形阵列的地面无人装备集群通信干扰抑制 [J ] . 兵工学报 , 2023 , 44 ( 5 ): 1414 - 1421 .

MU W W , HE B B , QI Y , et al. Communication interference suppression for ground unmanned equipment swarms based on circular array [J ] . Acta Armamentarii , 2023 , 44 ( 5 ): 1414 - 1421 . (in Chinese)

王浩同 , 刘白林 , 刘智平 , 等 . 基于区块链的无人机集群抗干扰通信模型 [J ] . 火力与指挥控制 , 2022 , 47 ( 1 ): 72 - 79 .

WANG H T , LIU B L , LIU Z P , et al. Anti-jamming communication model for UAV swarm based on blockchain [J ] . Fire Control & Command Control , 2022 , 47 ( 1 ): 72 - 79 . (in Chinese)

ZHANG J R , WANG G , SONG Y F . Task assignment of the improved contract net protocol under a multi-agent system [J ] . Algorithms , 2019 , 12 ( 4 ): 70 .

SAMIEI A , ISMAIL S , SUN L . Cluster-based Hungarian approach to task allocation for unmanned aerial vehicles [C ] //Proceedings of 2019 IEEE National Aerospace and Electronics Conference.Dayton,OH, US:IEEE , 2019 : 148 - 154 .

DAI J , CHEN W H , CHEN R X , et al. Research on task assignment algorithm of heterogeneous aircraft cooperative cluster in dynamic scene [J ] . Computers & Electrical Engineering , 2023 , 110 : 108781 .

WANG G , LÜ X , YAN X H . A two-stage distributed task assignment algorithm based on contract net protocol for multi-UAV cooperative reconnaissance task reassignment in dynamic environments [J ] . Sensors , 2023 , 23 ( 18 ): 7980 .

马培博 , 刘新 , 耿川 , 等 . 基于粒子群算法的集中式多无人机静态任务分配 [J ] . 无线电工程 , 2025 , 55 ( 1 ): 204 - 210 .

MA P B , LIU X , GENG C , et al. Centralized multi-UAV static task allocation based on particle swarm optimization algorithm [J ] . Radio Engineering , 2025 , 55 ( 1 ): 204 - 210 . (in Chinese)

LIU J , ANAVATTI S , GARRATT M , et al. A hierarchical mission planning system for multi-uncrewed ground vehicles using fast cost evaluation and ant colony optimisation [J ] . Information Sciences , 2024 , 679 : 121029 .

WANG Z H , ZHANG J L . A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method [J ] . Knowledge-Based Systems , 2022 , 250 : 109072 .

贾睿轩 , 陈晓明 , 邵书义 , 等 . 基于博弈论的异构无人集群协同编队避障控制 [J ] . 吉林大学学报(信息科学版) , 2024 , 42 ( 4 ): 1 - 10 .

JIA R X , CHEN X M , SHAO S Y , et al. Obstacle avoidance control of cooperative formation for heterogeneous unmanned swarm system with game-theoretic [J ] . Journal of Jilin University (Information Science Edition) , 2024 , 42 ( 4 ): 1 - 10 . (in Chinese).

曹子建 , 孙泽龙 , 闫国闯 , 等 . 基于强化学习的无人机集群对抗策略推演仿真 [J ] . 兵工学报 , 2023 , 44 ( 增刊2 ): 126 - 134 .

CAO Z J , SUN Z L , YAN G C , et al. Simulation of UAV swarm confrontation strategy deduction based on reinforcement learning [J ] . Acta Armamentarii , 2023 , 44 ( S2 ): 126 - 134 . (in Chinese)

MENG G L , ZHOU M Z , MENG T K , et al. Enhancing mission planning of large-scale UAV swarms with ensemble predictive model [J ] . Drones , 2024 , 8 ( 8 ): 362 .

SOLTANI A R , TAWFIK H , GOULERMAS J Y , et al. Path planning in construction sites:Performance evaluation of the Dijkstra,A * ,and GA search algorithms [J ] . Advanced Engineering Informatics , 2002 , 16 ( 4 ): 291 - 303 .

SONG R , LIU Y C , BUCKNALL R . Smoothed A * algorithm for practical unmanned surface vehicle path planning [J ] . Applied Ocean Research , 2019 , 83 : 9 - 20 .

NASH A , DANIEL K , KOENIG S , et al. Theta * :any-angle path planning on grids [J ] . Journal of Artificial Intelligence Research , 2010 , 39 : 533 - 579 .

NASH A , KOENIG S , TOVEY C . Lazy Theta * :any-angle path planning and path length analysis in 3D [C ] // Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence and the Twenty-Second Innovative Applications of Artificial Intelligence Conference,and the First Symposium on Educational Advances in Artificial Intelligence.Reston,VA , US : AAAI Press , 2011 .

ZHAO E Y , ZHOU N , LIU C J , et al. Time-aware MADDPG with LSTM for multi-agent obstacle avoidance:a comparative study [J ] . Complex & Intelligent Systems , 2024 , 10 ( 3 ): 4141 - 4155 .

苏梅梅 , 程咏梅 , 胡劲文 , 等 . 基于改进蚁群算法的无人机集群任务分配和路径规划联合优化 [J ] . 无人系统技术 , 2021 , 4 ( 4 ): 40 - 50 .

SU M M , CHENG Y M , HU J W , et al. Joint optimization of task allocation and path planning for UAV swarms based on improved ant colony algorithm [J ] . Unmanned Systems Technology , 2021 , 4 ( 4 ): 40 - 50 . (in Chinese)

FU G H , SONG Y X , WU Y J . Coupled optimization of UAV cluster path optimization and task assignment on a mobile platform [J ] . Mathematics , 2025 , 13 ( 1 ): 27 .

DONG X L , SHI C G , WEN W , et al. Multi-mission oriented joint optimization of task assignment and flight path planning for heterogeneous UAV cluster [J ] Remote Sensing , 2023 , 15 ( 22 ): 5315 .

Views

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption

Autonomous Multi-UAV Target Search Algorithm for Unknown Environments

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

A Mobile Robot Path Planning Algorithm Based on Ant Colony Optimization Guide Deep Q-Networks

AUV Obstacle Avoidance and Path Planning Based on Artificial Potential Field and Improved Reinforcement Learning

Related Author

GU Yuqi

LI Junqiu

YANG Yongxi

LI Xueping

WANG Zhifang

ZHU Shaohui

LIU Xinyang

LIN Min

Related Institution

National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology

College of Control Science and Engineering, Zhejiang University

Tianjin Navigation Equipment Research Institute

Polytechnic Institute of Zhejiang University, Zhejiang University

Shaanxi Aerospace Flight Vehicle Design Key Laboratory, School of Astronautics, Northwestern Polytechnical University

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：49231 Visits Today：28

⁰