摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于机器人的新报告。根据NewsRx编辑S在佛蒙特州伯灵顿的新闻报道，研究表明，"房主和技术人员对房屋维修的感知和认知是人类-建筑互动的主要例子。"这项研究的财政支持者包括广泛的机构公告计划和寒冷地区研究和工程实验室；海军研究办公室；Nsf；Nasa EPSCOR.我们的新闻记者从Vermo NT大学的研究中获得了一句话："损害、腐烂和虫害呈现出人类解读并采取行动补救和减轻的信号。维护认知过程对可持续性和经济活力有直接的影响。尽管家庭维护的实践可以追溯到远古，但它们很容易随着现代技术的发展而得到加强和改进。本文介绍了嵌入机器学习(ML)并以电子格式呈现的网络化智能技术的使用，以更好地告知房主和住户安全和维护问题。演示的技术包括受限区域的机器人探测、结构形状和变形的激光雷达扫描、湿度和气体传感、漏水检测、网络嵌入式ML，以及具有多用户协作能力的增强真实界面。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news reporting out of Burlington, Vermont, by NewsRx editor s, research stated, “Sensing and cognition by homeowners and technicians for hom e maintenance are prime examples of human-building interaction.” Financial supporters for this research include Broad Agency Announcement Program And Cold Regions Research And Engineering Laboratory; Office of Navy Research; Nsf; Nasa Epscor.Our news journalists obtained a quote from the research from University of Vermo nt: “Damage, decay, and pest infestation present signals that humans interpret a nd then act upon to remedy and mitigate. The maintenance cognition process has d irect effects on sustainability and economic vitality, as well as the health and well-being of building occupants. While home maintenance practices date back to antiquity, they readily submit to augmentation and improvement with modern tech nologies. This paper describes the use of networked smart technologies embedded with machine learning (ML) and presented in electronic formats to better inform homeowners and occupants about safety and maintenance issues, as well as recomme nd courses of remedial action. The demonstrated technologies include robotic sen sing in confined areas, LiDAR scans of structural shape and deformation, moistur e and gas sensing, water leak detection, network embedded ML, and augmented real ity interfaces with multi-user teaming capabilities.”
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