基于多微粒群优化的机器人气味源定位

doi:10.3969/j.issn.0372-2112.2013.10.021

电子学报 ›› 2013, Vol. 41 ›› Issue (10): 2000-2009.DOI: 10.3969/j.issn.0372-2112.2013.10.021

基于多微粒群优化的机器人气味源定位

巩敦卫, 戚成亮, 张勇, 胡滢

中国矿业大学信息与电气工程学院, 江苏徐州 221116

收稿日期:2012-08-28 修回日期:2013-01-09 出版日期:2013-10-25
- 作者简介:
- 巩敦卫 男.1970年3月出生,江苏铜山人.教授、博导,主要研究方向:智能控制、软件测试、交互式进化算法等. E-mail:dwgong@vip.163.com 戚成亮 男.1986年3月出生,江苏徐州人.硕士,主要研究方向:群体智能. E-mail:qi_liang76@yahoo.com.cn 张勇 男.1979年9月出生,山东莱芜人.副教授、博士,主要研究方向:智能控制、进化计算、机器人协作等. 胡滢 女.1988年4月出生,安徽黄山人.硕士研究生,主要研究方向:智能控制.
- 基金资助:
- 国家自然科学基金 (No.61005089); 江苏省自然科学基金 (No.BK2008125); 高等学校博士学科点专项科学研基金 (No.20100095120016)

Localizing Odor Sources Using Robots Based on Multi-swarm Particle Swarm Optimization

GONG Dun-wei, QI Cheng-liang, ZHANG Yong, HU Ying

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

Received:2012-08-28 Revised:2013-01-09 Online:2013-10-25 Published:2013-10-25
- Supported by:
- National Natural Science Foundation of China (No.61005089); Natural Science Foundation of Jiangsu Province, China (No.BK2008125); Specialized Research Fund for Doctoral Program of Higher Education (No.20100095120016)

摘要/Abstract

摘要： 研究多气味源同时定位问题,提出一种基于多微粒群优化的机器人气味源定位方法.该方法将机器人看作一个微粒,邻近的微粒组成一个子群,不同的子群定位不同的气味源.通过合并相似的子群和降低微粒在已搜索区域的适应值,使得微粒群定位尽可能多的气味源.当气味源所在环境变化时,根据子群当代极值与前代全局极值之间的关系,选择子群的全局极值.将所提方法应用于3个典型静态环境与1个动态环境的气味源定位,并与5种已有方法比较.实验结果表明,所提方法能够高效地定位多气味源.

关键词: 气味源定位, 机器人, 微粒群优化, 子群合并, 适应值调整

Abstract: The problem of localizing multiple odor sources is focused on,and a method of localizing odor sources using robots based on multi-swarm particle swarm optimization(PSO)is presented in this paper.In this method,each robot is regarded as a particle,neighboring particles form a sub-swarm,and different sub-swarms are used to localize different odor sources.In order to make the whole swarm localize as many odor sources as possible,the merging strategy of similar sub-swarms and the reducing strategy of the individual fitness are incorporated into the proposed algorithm.When the environment in which these odor sources lie changes,the globally optimal solution of a sub-swarm is selected according to the relationship between the optimal solution of the sub-swarm in the current generation and that up to the previous generation.The proposed method is applied to localize odor sources in three typical static environments and one dynamic environment,and compared with five previous methods.The experimental results confirm that the proposed method can efficiently localize odor sources.

Key words: odor source localization, robot, PSO, sub-swarm emergence, fitness adjustment

中图分类号:

TP301

巩敦卫, 戚成亮, 张勇, 等. 基于多微粒群优化的机器人气味源定位[J]. 电子学报, 2013, 41(10): 2000-2009.

GONG Dun-wei, QI Cheng-liang, ZHANG Yong, et al. Localizing Odor Sources Using Robots Based on Multi-swarm Particle Swarm Optimization[J]. Acta Electronica Sinica, 2013, 41(10): 2000-2009.

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基于多微粒群优化的机器人气味源定位

Localizing Odor Sources Using Robots Based on Multi-swarm Particle Swarm Optimization

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