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挺进深蓝:从单体仿生到群体智能
Advance into Ocean: From Bionic Monomer to Swarm Intelligence

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文摘 近年来,群体智能作为一项多学科融合的新技术在各领域的研究成果斐然,例如共享出行、蜂群无人机系统、水下多智能体平台等,但与水下场景结合的群体智能技术缺乏系统的归纳,有必要对水下群体智能技术的发展现状和趋势进行讨论和分析.本文对群体智能理论进行了详尽的分析,给出了群体智能的完整概念、具体算法以及应用领域.文中指出,为解决海洋复杂环境对探测、通信等造成的一系列困难,需要将群体智能技术应用于水下场景.本文就国内外水下群体智能技术的研究现状进行了总结,对水下群体智能存在的环境复杂、通信受限、信息获取困难、系统能力不足以及能量供应受限的难点进行了评述.针对这些难点,本文对结合群体智能理论的时变环境感知技术、传感网络设计、协同导航定位技术、路径规划技术、水下编队控制以及分布式自主决策技术进行了分析,并在文末给出水下群体智能技术未来在跨域通信、多平台异构、自主作业能力革新方面的发展趋势和展望.
其他语种文摘 In recent years, swarm intelligence, as a novel multi-disciplinary technology, has made remarkable achievements in various fields, such as shared traffic, swarm unmanned aerial vehicle system, underwater multi-agent platform, and so on, but the swarm intelligence technology combined with underwater scene is lack of systematic induction. Therefore, it is necessary to discuss and analyze the development status and trend of underwater swarm intelligence technology. This review aims to study and summarize the underwater swarm intelligence technology. It makes a complete introduction to the swarm intelligence theory, and gives the complete concept, specific algorithm and application field of swarm intelligence. And swarm intelligence technology needs to be applied to underwater scenes in order to solve a series of difficulties caused by complex marine environment. This review summarizes the research status of underwater swarm intelligence technology at home and abroad, and comments on the difficulties of underwater swarm intelligence, such as complex environment, limited communication, difficult information acquisition, insufficient system capacity and limited energy supply. In addition, aiming at these difficulties, this review analyzes the time-varying environment perception technology, underwater sensor network, collaborative navigation and positioning technology, path planning technology, underwater formation control and distributed autonomous decision-making technology combined with swarm intelligence theory, and gives the future application of underwater swarm intelligence technology in cross domain communication, multi-platform heterogeneous development trend and prospect of innovation of independent operation ability.
来源 电子学报 ,2021,49(12):2458-2467 【核心库】
DOI 10.12263/DZXB.20201448
关键词 群体智能 ; 水下机器人 ; 智能控制 ; 多智能体 ; 自主潜航器 ; 粒子群算法 ; 分布式系统 ; 水下通信
地址

清华大学电子工程系, 北京, 100084

语种 中文
文献类型 研究性论文
ISSN 0372-2112
学科 电子技术、通信技术
文献收藏号 CSCD:7127424

参考文献 共 50 共3页

1.  Kennedy J. Swarm Intelligence,2006 CSCD被引 10    
2.  Dorigo M. Ant colony optimization. IEEE Computational Intelligence Magazine,2006,1(4):28-39 CSCD被引 245    
3.  Kennedy J. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks,1995:1942-1948 CSCD被引 281    
4.  Reynolds C W. Flocks, birds and schools: a distributed behavioral model. Computer Graphics,1987,21(1):25-34 CSCD被引 310    
5.  Mishra A K. PSO-GWO optimized fractional order PID based hybrid shunt active power filter for power quality improvements. IEEE Access,2020,8:74497-74512 CSCD被引 2    
6.  Gandomi A H. Construction cost minimization of shallow foundation using recent swarm intelligence techniques. IEEE Transactions on Industrial Informatics,2018,14(3):1099-1106 CSCD被引 4    
7.  Lu J. Self-tuning PID Control Scheme with Swarm Intelligence Based on Support Vector Machine. 2014 IEEE International Conference on Mechatronics and Automation,2014:1554-1558 CSCD被引 1    
8.  Jun G. On dynamic evolution of industry agglomeration based on swarm intelligence. 2008 International Seminar on Future Information Technology and Management Engineering,2008:24-28 CSCD被引 1    
9.  Chan K Y. Prediction of short-term traffic variables using intelligent swarm-based neural networks. IEEE Transactions on Control Systems Technology,2013,21(1):263-274 CSCD被引 6    
10.  Renfrew D. Traffic signal control with swarm intelligence. 2009 Fifth International Conference on Natural Computation,2009:79-83 CSCD被引 1    
11.  Li Q. Traffic flow guidance and optimization of connected vehicles based on swarm intelligence. 2019 Chinese Control Conference,2019:2099-2104 CSCD被引 1    
12.  Mukherjee A. Adaptive particle swarm optimization based energy efficient dynamic correlation behavior of secondary nodes in cognitive radio sensor networks. IET Communications,2020,14(10):1658-1665 CSCD被引 1    
13.  孙家泽. 群体智能算法及在三维文物虚拟拼接中的应用,2015 CSCD被引 2    
14.  Bharne P K. Data clustering algorithms based on swarm intelligence. 2011 3rd International Conference on Electronics Computer Technology,2011:407-411 CSCD被引 1    
15.  Jin P. A clustering algorithm for data mining based on swarm intelligence. 2007 International Conference on Machine Learning and Cybernetics,2007:803-807 CSCD被引 1    
16.  Kusyk J. AI based flight control for autonomous UAV Swarms. 2018 International Conference on Computational Science and Computational Intelligence,2018:1155-1160 CSCD被引 1    
17.  Arnold R. Heterogeneous UAV multirole swarming behaviors for search and rescue. 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management,2020:122-128 CSCD被引 1    
18.  李建军. 基于群体智能的多AUV协同任务分配方法研究,2018 CSCD被引 2    
19.  韩青. 基于纯角度观测信息的多机器人编队控制方法研究,2018 CSCD被引 1    
20.  张守旭. 半仿生机器鱼建模与编队控制研究,2017 CSCD被引 1    
引证文献 8

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CSCD被引 16

2 潘弘洋 基于新一代通信技术的无人机系统群体智能方法综述 吉林大学学报. 工学版,2023,53(3):629-642
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