Segmenting areas of potential contamination for adaptive robotic disinfection in built environments
Segmenting areas of potential contamination for adaptive robotic disinfection in built environments
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DOI：
10.1016/j.buildenv.2020.107226
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发表时间：
2020-10-15
影响因子：
7.4
通讯作者：
He, Qiang
中科院分区：
文献类型：
--
作者：
Hu, Da;Zhong, Hai;He, Qiang
医院、学校和机场等大规模聚集的建筑环境可能成为病原体传播和暴露的热点。消毒对于降低感染风险和预防传染病爆发至关重要。然而，清洁及消毒是劳动密集型、耗时且有损健康的工作，尤其是在二零一九年冠状病毒病大流行期间。为了应对这一挑战，本研究提出了一种新的框架，使机器人消毒在建筑环境中，以减少病原体的传播和暴露。首先，一个同时定位和映射技术是利用机器人导航在建筑环境中。其次，开发了一种深度学习方法，以基于对象启示概念在三维中分割和映射潜在污染区域。第三，使用短波长紫外光，生成机器人消毒的轨迹，以适应潜在污染区域的几何形状，以确保完全和安全的消毒。仿真和物理实验进行了验证所提出的方法，这表明了智能机器人消毒的可行性，并强调了在大规模聚集的建筑环境中的适用性。
Mass-gathering built environments such as hospitals, schools, and airports can become hot spots for pathogen transmission and exposure. Disinfection is critical for reducing infection risks and preventing outbreaks of infectious diseases. However, cleaning and disinfection are labor-intensive, time-consuming, and health-undermining, particularly during the pandemic of the coronavirus disease in 2019. To address the challenge, a novel framework is proposed in this study to enable robotic disinfection in built environments to reduce pathogen transmission and exposure. First, a simultaneous localization and mapping technique is exploited for robot navigation in built environments. Second, a deep-learning method is developed to segment and map areas of potential contamination in three dimensions based on the object affordance concept. Third, with short-wavelength ultraviolet light, the trajectories of robotic disinfection are generated to adapt to the geometries of areas of potential contamination to ensure complete and safe disinfection. Both simulations and physical experiments were conducted to validate the proposed methods, which demonstrated the feasibility of intelligent robotic disinfection and highlighted the applicability in mass-gathering built environments.