Mapping and Localization of Multiple Odor Sources with Evidence Theory in Outdoor Environments
LI Jigong1,2, YANG Jing1,2, ZHOU Jieyong1,2, LIU Jia1,2, YANG Li1,2
1. School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China;
2. Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin 300222, China
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.
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).
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