室外环境下基于证据理论的多气味源测绘及定位  

Mapping and Localization of Multiple Odor Sources with Evidence Theory in Outdoor Environments

作　　者：李吉功[1,2] 杨静 周洁勇 刘佳 杨丽[1,2] LI Jigong;YANG Jing;ZHOU Jieyong;LIU Jia;YANG Li(School of Automation and Electrical Engineering,Tianjin University of Technology and Education,Tianjin 300222,China;Tianjin Key Laboratory of Information Sensing and Intelligent Control,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学自动化与电气工程学院,天津300222 [2]天津市信息传感与智能控制重点实验室,天津300222

出　　处：《机器人》2019年第6期771-778,787,共9页Robot

基　　金：国家自然科学基金(61573253,61601197);天津市自然科学基金(15JCQNJC04200)

摘　　要：采用单个移动机器人对室外自然环境下的多气味源定位问题进行了研究.首先构建嗅觉感知模型,将机器人在每个采样周期中测得的气味浓度和风速/风向信息融合为局部区域内是否存在气味源的证据.然后采用证据理论将该证据与已有证据进行组合,从而更新气味源的空间分布图.最后在室外自然环境下进行实验,结果表明所提出的嗅觉感知模型可用于时变流场下的多气味源在线测绘.对比IP(independence of posteriors)算法(一种贝叶斯占用栅格测绘方法),所提出的基于证据理论的测绘方法具有较好的鲁棒特性.The localization of multiple odor sources using a mobile robot in a natural airflow environment is studied.Firstly,the olfactory perception model is constructed,and the odor concentration and airflow direction/speed detected by the robot in each time step are fused as the evidence about the existence of odor sources in local area.Then,the evidence is combined with the existing evidences by using the evidence theory to update the spatial distribution of the multiple odor sources.The experimental results in outdoor natural environment show that the proposed olfactory perception model is applicable to the online mapping of multiple odor sources in environment with time-varying airflow,and the proposed mapping method based on the evidence theory can achieve a better robustness compared with the IP(independence of posteriors)algorithm(a Bayesian occupancy grid mapping method).

关 键 词：移动机器人 多气味源 嗅觉感知模型 证据理论 室外气流环境 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]