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基于动态紫外线C辐射地图的自主环境消毒

Autonomous Environment Disinfection Based on Dynamic UV-C Irradiation Map.

作者信息

Mantelli Mathias, Dos Santos Leticia, de Fraga Lucas, Miotto Giovanna, Bergamin Augusto, Cardoso Etevaldo, Serrano Miguel, Maffei Renan, Prestes Edson, Netto Joao, Kolberg Mariana

机构信息

Institute of InformaticsUniversidade Federal do Rio Grande do Sul Porto Alegre 90650-001 Brazil.

Instor Robotics and Projects 94410-970 Porto Alegre Brazil.

出版信息

IEEE Robot Autom Lett. 2022 Feb 22;7(2):4789-4796. doi: 10.1109/LRA.2022.3152719. eCollection 2022 Apr.

Abstract

The COVID-19 pandemic has become a worldwide concern and has motivated the entire scientific community to join efforts to fight it. Studies have shown that SARS-CoV-2 remains viable onsurfaces for days, increasing the chances of human infection. Environmental disinfection is thus an important action to prevent the transmission of the virus. Despite the valuable contribution of the research community to the field of UV-C disinfection by robots, there still lacks a disinfection system that is fully autonomous and computes its trajectory in real-time and in unknown environments. To meet this need, we propose an autonomous UV-C disinfection strategy for indoor environments based on a dynamic Irradiation Map that indicates the amount of energy applied in each region. Our method was tested in different scenarios and compared with other disinfection strategies. Experiments show that our approach delivers better results, especially when targeting high ideal UV-C doses.

摘要

新冠疫情已成为全球关注的问题,并促使整个科学界共同努力抗击疫情。研究表明,严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在物体表面可存活数天,增加了人类感染的几率。因此,环境消毒是预防病毒传播的一项重要措施。尽管研究界对机器人紫外线C消毒领域做出了宝贵贡献,但仍缺乏一种完全自主且能在未知环境中实时计算其轨迹的消毒系统。为满足这一需求,我们基于动态辐照图提出了一种针对室内环境的自主紫外线C消毒策略,该图显示了每个区域施加的能量。我们的方法在不同场景下进行了测试,并与其他消毒策略进行了比较。实验表明,我们的方法能取得更好的效果,尤其是在针对高理想紫外线C剂量时。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea0/9014471/6e8d7daa888c/mante1-3152719.jpg

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