室外环境下基于证据理论的多气味源测绘及定位

doi:10.13973/j.cnki.robot.180779

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李吉功, 杨静, 周洁勇, 刘佳, 杨丽. 室外环境下基于证据理论的多气味源测绘及定位[J]. 机器人, 2019, 41(6): 771-778,787.DOI: 10.13973/j.cnki.robot.180779. LI Jigong, YANG Jing, ZHOU Jieyong, LIU Jia, YANG Li. Mapping and Localization of Multiple Odor Sources with Evidence Theory in Outdoor Environments. ROBOT, 2019, 41(6): 771-778,787. DOI: 10.13973/j.cnki.robot.180779.

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．

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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

Abstract：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).

Key words： mobile robot multiple odor sources olfactory perception model evidence theory outdoor airflow environment

收稿日期: 2018-12-29 录用日期: 2019-07-16

TP24

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

通讯作者: 李吉功,charles75@163.com E-mail: charles75@163.com

作者简介: 李吉功(1975-),男,博士,教授.研究领域:机器人主动嗅觉,机器人环境感知,导航与路径规划.
杨静(1979-),女,硕士,实验师.研究领域:软件工程,算法设计与实现.
周洁勇(1988-),男,硕士,工程师.研究领域:机器人主动嗅觉,机器人环境感知,算法设计.

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.180779 或 https://robot.sia.cn/CN/Y2019/V41/I6/771

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