Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities
Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities
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DOI：
10.1111/mice.12811
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Da Hu;Shuai Li
中科院分区：
文献类型：
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作者：
Da Hu;Shuai Li
现有的消毒机器人不够智能，无法根据物体表面材料调整动作，实现精确有效的消毒。为了解决这个问题，开发了一个新的框架，使机器人能够识别各种物体表面材料，并适应其消毒方法，以兼容已识别的物体表面材料。具体而言，提出了一种新的深度学习网络，该网络集成了多层次和多尺度特征，以对需要消毒的污染表面上的材料进行分类。计算污染表面的感染风险，以选择合适的消毒模式和参数。开发的材料识别方法展示了最先进的性能，在上下文数据库验证和测试数据集上分别实现了92.24%和91.84%的准确度。该方法还在医疗机构的背景下进行了测试和评估，其中材料分类的准确率达到89.09%，并成功实施了自适应机器人消毒。
Existing disinfection robots are not intelligent enough to adapt their actions to object surface materials for precise and effective disinfection. To address this problem, a new framework is developed to enable the robot to recognize various object surface materials and to adapt its disinfection methods to be compatible with recognized object surface materials. Specifically, a new deep learning network is proposed that integrates multi‐level and multi‐scale features to classify the materials on contaminated surfaces requiring disinfection. The infection risk of contaminated surfaces is computed to choose the appropriate disinfection modes and parameters. The developed material recognition method demonstrates state‐of‐the‐art performance, achieving an accuracy of 92.24% and 91.84% on the Materials in Context Database validation and test datasets, respectively. The proposed method was also tested and evaluated in the context of healthcare facilities, where the material classification achieved an accuracy of 89.09%, and the adaptive robotic disinfection was successfully implemented.