[1]程磊,周明达,吴怀宇,等.无线传感器环境下粒子群优化的多机器人协同定位研究[J].智能系统学报,2015,10(01):138-142.[doi:10.3969/j.issn.1673-4785.201310067]
 CHENG Lei,ZHOU Mingda,WU Huaiyu,et al.Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor[J].CAAI Transactions on Intelligent Systems,2015,10(01):138-142.[doi:10.3969/j.issn.1673-4785.201310067]
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无线传感器环境下粒子群优化的多机器人协同定位研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年01期
页码:
138-142
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Cooperative multi-robot localization based on particle swarm optimization in the environment of wireless sensor
作者:
程磊12 周明达1 吴怀宇1 李杰1 王永骥3
1. 武汉科技大学 冶金自动化与检测技术教育部工程研究中心, 湖北 武汉 430081;
2. 北京大学 工学院, 北京 100871;
3. 华中科技大学 自动化学院, 湖北 武汉 430074
Author(s):
CHENG Lei12 ZHOU Mingda1 WU Huaiyu1 LI Jie1 WANG Yongji3
1. Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China;
2. College of Engineering, Peking University, Beijing 100871, China;
3. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
关键词:
粒子群优化多机器人协同定位无线传感器网络重采样惯性权重多信息融合适应度函数
Keywords:
particle swarm optimizationmulti-robotcooperative localizationwireless sensor networkresamplinginertia weightmultiple information fusionfitness function
分类号:
TP31
DOI:
10.3969/j.issn.1673-4785.201310067
文献标志码:
A
摘要:
协同定位是多机器人自主行为的一项重要技术,重点描述了无线传感器网络环境下结合粒子群优化提出多机器人协同定位算法。该算法引入重采样,解决了粒子耗尽问题,扩大了解空间的范围,保证了种群的多样性,并且引入了惯性权重解决了粒子退化的问题。仿真结果表明,利用无线传感器网络进行辅助导航,采用粒子群优化算法,综合无线传感器网络进行辅助导航,融合各个机器人观测信息,可以降低求解问题的空间维数,在高斯噪声下能有效提高移动机器人定位精度。
Abstract:
Cooperative localization is an important technique of multi-robot’s autonomous behavior. In this paper, the multi-robot cooperative localization algorithm based on the optimization of particle swarm optimization under wireless sensor network environment is described. The resampling algorithm is introduced to solve the problem of particle depletion, enlarge the scope of solution space and guarantee the diversity of population. The introduction of inertia weight provides a solution for the particle degradation. Simulation results showed that by using the particle swarm optimization algorithm, which is supported by wireless sensor network to assist navigation and integrating robots’ observation information, the spatial dimensions of the problem can be reduced. In addition, the accuracy of robot localization can be improved effectively under the background of Gaussian noise.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-10-24;改回日期:。
基金项目:国家自然科学基金资助项目(60705035,61075087,61203331);湖北省重点实验室开放基金重点资助项目(Z201102);河南省高等学校控制工程重点学科开放基金资助项目(KG2011-01);湖北省教育厅科研计划重点资助项目(D20131105);湖北省科技计划自然科学基金重点资助项目(2010CDA005).
作者简介:程磊,男,1976生,副教授,中国人工智能学会青年工作委员会常务委员,中国人工智能学会智能机器人专业委员会委员,湖北省人工智能学会理事,主要研究方向为机器人及复杂系统。主持包括国家自然科学基金项目在内的省部级以上项目9项,获国家教学成果二等奖1项,获学术论文奖6项,发表学术论文40余篇,编著教材2部;周明达,男,1989生,主要研究方向为机器人基础运动控制及其人工智能控制;吴怀宇,男,1961生,教授,博士生导师,主要研究方向为服务机器人及其控制。国家及省部级基金7项,国际合作项目3项以及横向课题8项,获省部级科技进步一等奖2项;教育部科技成果鉴定2项,获湖北省自然科学优秀学术论文一等奖1项,国家发明专利2项,实用新型专利2项,发表学术论文50余篇,出版专著1部,主编教材3部。
通讯作者:程磊.E-mail:chenglei@wust.edu.cn.
更新日期/Last Update: 2015-06-16