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

作　　者：巩敦卫[1] 戚成亮[1] 张勇[1] 胡滢[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《电子学报》2013年第10期2000-2009,共10页Acta Electronica Sinica

基　　金：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)

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.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 slrategy of similar sub-swarms and the reducing strat- egy 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 re- sults confirm that the proposed method can efficiently localize odor sources.

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 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]