[1]杨 茂,李成凤,田彦涛.群体机器人同步问题的分布式协同控制及优化[J].智能系统学报,2010,5(03):247-253.
 YANG Mao,LI Cheng-feng,TIAN Yan-tao.Distributed coadaptive control and optimization ofswarm robot synchronization[J].CAAI Transactions on Intelligent Systems,2010,5(03):247-253.
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群体机器人同步问题的分布式协同控制及优化(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第5卷
期数:
2010年03期
页码:
247-253
栏目:
出版日期:
2010-06-25

文章信息/Info

Title:
Distributed coadaptive control and optimization ofswarm robot synchronization
文章编号:
1673-4785(2010)03-0247-07
作者:
杨 茂1李成凤1田彦涛12
1.吉林大学 通信工程学院,吉林 长春 130025;
2.吉林大学 工程仿生教育部重点实验室,吉林 长春 130025
Author(s):
YANG Mao1 LI Cheng-feng1 TIAN Yan-tao12
1. School of Communication Engineering, Jilin University, Changchun 130025, China;
2.Key Laboratory of Bionic Engineering (Jilin University), Ministry of Education, Changchun 130025, China
关键词:
群体机器人同步协同控制分布式控制器粒子群优化算法
Keywords:
swarm robot synchronization cooperative control distributed controller PSO algorithms
分类号:
TP18
文献标志码:
A
摘要:
主要研究群体机器人系统协同适应性,通过局部信息交互下的分布式控制实现群体对复杂环境的适应.以同步现象为研究对象,结合虚拟力、最近邻居原则以及环境因素,提出了一种可以解释该现象的分布式控制器.并证明该控制器能够使所有的群体成员在误差允许的范围内收敛到一个共同速度,其优点是仅需要局部信息就能够实现稳定的群体行为.此外,在上述分布式控制器设计的基础上进行了参数优化,利用粒子群优化算法实现能量消耗最少的目标.通过仿真实验验证了控制器及算法的可行性和有效性.
Abstract:
Coadaptive control mechanisms for swarm robot systems were investigated to see if swarms could adapt to complex environments by using distributed control with local information exchange. The phenomenon of synchronization was studied, and on that basis a decentralized controller was proposed. It combined the ideas of a virtual force, the nearest neighborhood law, and environmental factors. It was proven that this controller can enable all swarm members to converge to a common velocity with bounded errors, whether the swarm topology is fixed or dynamic. The advantage of this controller is that it just needs local information to provide stable group behavior. In addition, parameters were optimized on the basis of the proposed controller to achieve the goal of minimum energy consumption. To deal with particle swarm optimization algorithms (PSO) easily falling into local optimums and having low accuracy, an improved algorithm was put forward. This was used to solve the energy optimization problem. Simulation results are included that verified the controller and algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2009-11-12.
基金项目:国家自然科学基金资助项目(60675057);吉林大学研究生创新基金资助项目(20091020).
通信作者:田彦涛.
E-mail: tianyt@jlu.edu.cn.
作者简介:
杨茂,男,1982年生,博士研究生,主要研究方向为群体机器人系统、强化学习.
李成凤,女,1986年生,硕士研究生,主要研究方向为群体机器人系统、分布式优化.
 田彦涛,男,1958年生,教授、博士生导师、博士.吉林大学自动化研究所所长,兼任中国自动化学会理事、中国自动化学会机器人专业委员会常务委员、吉林省自动化学会理事长、吉林省通信学会副理事长、吉林省电机工程学会常务理事,中科院沈阳自动化研究所先进制造技术实验室学术委员会委员,中国自动化学会《机器人》学报编委、《吉林大学学报(信息科学版)》副主编.主要研究方向为复杂系统建模、优化与控制、分布式智能系统与网络控制.近五年,完成国家“863”计划项目1项、国家自然科学基金项目1项、吉林省科技发展计划项目3项、国家“863”计划智能机器人网点实验室基金项目1项;目前负责承担国家“863”计划项目和国家自然科学基金项目等国家级科研项目3项、吉林省科技发展计划重点项目3项.曾被评为国家机械部“优秀科技青年”、机械部和教育部跨世纪学科带头人,2004年评为吉林省拔尖创新人才.发表学术论文70余篇,其中被SCI、EI、ISTP检索36篇.
更新日期/Last Update: 2010-08-27