基于改进K-means算法和总时最短机制的无人机群多目标分配围猎策略 -- 西北工业大学学报,2022,40(6):1297-1304

	论文:2022,Vol:40,Issue(6):1297-1304
	引用本文：
	胡滨, 朱亚辉, 杜致泽, 赵子昕, 周延年. 基于改进K-means算法和总时最短机制的无人机群多目标分配围猎策略[J]. 西北工业大学学报
	HU Bin, ZHU Yahui, DU Zhize, ZHAO Zixin, ZHOU Yannian. Multi-target assignment hunting strategy of UAV swarm based on improved K-means algorithm and shortest time mechanism[J]. Journal of Northwestern Polytechnical University

基于改进K-means算法和总时最短机制的无人机群多目标分配围猎策略

胡滨¹, 朱亚辉², 杜致泽³, 赵子昕⁴, 周延年⁵

1. 西北工业大学自动化学院, 陕西西安, 710072;
2. 陕西学前师范学院数学与统计学院, 陕西西安 710061;
3. 西北工业大学机电学院, 陕西西安, 710072;
4. 交通运输通信信息集团有限公司卫星通信事业部, 北京 100011;
5. 空军工程大学防空反导学院, 陕西西安 710043

摘要:

无人机(UAV)群多目标围猎是一种重要的战术手段,提出了一种基于改进K-means和总时最短机制的围猎策略。大规模的任务分配问题结构复杂、解算难度大,为了得到较高的围猎效率,减少单机计算量,采用混合式的体系结构将复杂的多目标围猎问题逐步分解为UAV个体需要执行的任务集合,降低了系统的耦合性和任务解算的复杂度。该策略利用改进的K-means算法将多目标围猎问题分层,形成多个独立的单目标围猎子系统。在子系统内部将单目标围猎任务分解为多个UAV容易执行的子任务,并以总时最短机制在子任务和UAV之间建立一一对应的匹配关系,各UAV只需执行待执行的子任务即可达到多目标围猎的目的。仿真实验表明,多无人机群可以有效地对多个目标的围捕任务进行合理分配,证明了该分配策略的有效性。

关键词: 多目标围猎任务分配策略无人机群 K-means法

Multi-target assignment hunting strategy of UAV swarm based on improved K-means algorithm and shortest time mechanism

HU Bin¹, ZHU Yahui², DU Zhize³, ZHAO Zixin⁴, ZHOU Yannian⁵

1. School of Automation, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Mathematics and Statistics, Shaanxi Xueqian Normal University, Xi'an 710061, China;
3. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
4. Department of Satellite Communication, China Transport Telecommunication Information Group Co., Ltd, Beijing 100011, China;
5. School of Air Defense and Anti-Missile, Air Force Engineering University, Xi'an 710043, China

Abstract:

Multi-target hunting of UAV swarm is an important tactical means. This paper proposes a hunting strategy based on improved K-means and the shortest time mechanism. The large-scale task assignment problem is complex in structure and difficult to solve. To obtain higher hunting efficiency and reduce the amount of calculation on the single UAV, the hybrid architecture is used to decompose the complex multi-target hunting problem into a set of tasks that the UAV need to perform, which reduces the coupling of the system and the complexity of problem. Firstly, the multi-target hunting problem is stratified by the improved K-means algorithm to form multiple independent single target hunting subsystems. In the subsystem, the single target hunting task is decomposed into multiple subtasks that are easy to be executed by UAVs, and a one-to-one matching relationship between subtasks and UAVs is established by using the shortest time mechanism. UAV swarm can achieve multi-target hunting only by executing subtasks. The simulation results show that the UAV swarm can effectively allocate the multi-target hunting problem, which proves the effectiveness of the allocation strategy is proved.

Key words: multi-target hunting task allocation strategy UAV swarm K-means algorithm

收稿日期: 2022-04-01 修回日期:

DOI: 10.1051/jnwpu/20224061297

通讯作者: 周延年(1981—),空军工程大学讲师,主要从事综合评价研究。e-mail:83632893@qq.com Email：83632893@qq.com

作者简介: 胡滨(1977—),西北工业大学博士研究生,主要从事控制理论和人工智能研究

相关功能

PDF(2709KB) Free

打印本文

把本文推荐给朋友

作者相关文章

胡滨在本刊中的所有文章

朱亚辉 在本刊中的所有文章

杜致泽 在本刊中的所有文章

赵子昕 在本刊中的所有文章

周延年 在本刊中的所有文章


	参考文献:
	[1] THOMAS Lemaire, RACHID Alami, SIMON Lacroix. A distributed task allocation scheme in multi-UAV context[C]//IEEE International Conference on Robotics and Automation, 2004 [2] LIANG H, ZHANG L, SUN Y, et al. Containment control of semi-Markovian multiagent systems with switching topologies[J]. IEEE Trans on Systems, Man, and Cybernetics, 2021, 51(6):3889-3899 [3] LI Zhongkui, WEi Ren, LIU Xiangdong, et al. Distributed containment control of multi-agent systems with general linear dynamics in the presence of multiple leaders[J]. International Journal of Robust and Nonlinear Control, 2013, 23(5):534-547 [4] 张宇, 张琰, 邱绵浩, 等. 地空无人平台协同作战应用研究[J]. 火力与指挥控制,2021,46(5):6 ZHANG Yu, ZHANG Yan, QIU Mianhao, et al. Research on the ground-air unmanned platform cooperative combat application[J]. Fire Control & Command Control, 2021, 46(5):6 (in Chinese) [5] MURO C, ESCOBEDO R, SPECTOR L, et al. Wolf-pack(canis lupus) hunting strategies emerge from simple rules in computational simulations[J]. Behavioural Processes, 2011, 88(3):192-197 [6] ATAMURAT K, GAFURJAN I, MASSIMILIANO F. Simple motion pursuit and evasion differential games with many pursuers on manifolds with euclidean metric[J]. Discrete Dynamics in Nature and Society, 2016, 2016:1-8 [7] CHEN Jie, ZHA Wenzhong, PENG Zhihong, et al. Multi-player pursuit-evasion games with one superior evader[J]. Automatica, 2016, 71:24-32 [8] LOPEZ V G, LEWIS F L, WAN Y, et al. Solutions for multiagent pursuit-evasion games on communication graphs:finite-time capture and asymptotic behaviors[J]. IEEE Trans on Automatic Control, 2020, 65(5):1911-1923 [9] AMINI A, ASIF A, MOHAMMADI A. Formation-containment control using dynamic event-triggering mechanism for multi-agent systems[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 7(5):1235-1248 [10] HOLLAND J H. Adaptation in natural and artificial systems:an introductory analysis with application to biology, control and artificial intelligence[M]. Cambridge:The MIT Press, 1975 [11] YU D, XU H, CHEN C, et al. Dynamic coverage control based on K-means[J]. IEEE Trans on Industrial Electronics, 2021, 99:1 [12] KIM J, JU C, SON H I. A multiplicatively weighted Voronoi-based workspace partition for heterogeneous seeding robots[J]. Electronics, 2020, 9(11):1813 [13] PAOLO B, ANTONIO C, LUCA F, et al. Scalable and cost-effective assignment of mobile crowdsensing tasks based on profiling trends and prediction:the participact living lab experience[J]. Sensors, 2015, 15(8):18613-18640 [14] DOULAMIS N D, KOKKINOS P, VARVARIGOS E M. Resource selection for tasks with time requirements using spectral clustering[J]. IEEE Trans on Computers, 2014, 63(2):461-474 [15] ELANGO M, NACHIAPPAN S, TIWARI M K. Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms[J]. Expert Systems with Applications, 2011, 38(6):6486-6491 [16] BAI X, YAN W, CAO M. Clustering-based algorithms for multi-vehicle task assignment in a time-invariant drift field[J]. IEEE Robotics & Automation Letters, 2017, 2(4):2166-2173 [17] WANG B, CHEN W, ZHANG B, et al. Cooperative control-based task assignments for multiagent systems with intermittent communication[J]. IEEE Trans on Industrial Informatics, 2021, 17(10):6697-6708 [18] LEE D H, ZAHEER S A, KIM J H. A Resource-oriented, decentralized auction algorithm for multirobot task allocation[J]. IEEE Trans on Automation Science and Engineering, 2015, 12(4):1469-1481 [19] BAI X, CAO M, YAN W. Event-and time-triggered dynamic task assignments for multiple vehicles[J]. Autonomous Robots, 2020, 44(5):877-888 [20] CAO X, XU X. Hunting algorithm for multi-AUV based on dynamic prediction of target trajectory in 3D underwater environment[J]. IEEE Access, 2020, 8:1-1

邮编:710072 电话：029-88495455 Email：xuebao@nwpu.edu.cn

本系统由北京仁和汇智信息技术有限公司设计开发技术支持：info@rhhz.net