MICRO-BODD: A Miniaturized Intelligent Construction Robot for Optimal Building Operations and Defect Detection

MICRO-BODD：用于优化建筑操作和缺陷检测的小型化智能建筑机器人

• 批准号: 577219-2022
• 负责人: Zou, ZhengboZ
• 金额: $ 35.19万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749288
• 关键词: MICRO; BODD; Miniaturized; Intelligent; Construction

Buildings are responsible for 25% of energy consumption and 13% of greenhouse gas (GHG) emissions in Canada. Defects in building systems (e.g., leaks and blockages in heating, ventilation, and air conditioning; HVAC) and façades (e.g., thermal bridges) can cause energy losses of up to 30%. Furthermore, construction defects such as broken duct can cause harmful contaminants (e.g., glass fibre) to enter open spaces, negatively impacting occupants' health. To reduce the environmental consequences of the building sector, building owners, facility managers, and municipalities are seeking cost- and time-efficient methods to (1) detect defects in building systems and façades; and (2) use proactive maintenance approaches to reduce energy consumption and GHG emissions. We have assembled an interdisciplinary team of early career researchers with expertise in robotic design and control, building system optimization, life-cycle assessment, sustainable infrastructure management, and human neuroscience to revamp the current processes for building facility management. We will reimagine a future of carbon-neutral buildings through inputs from flexible robots and the intuitive human robot interaction. Our long-term aim is to reduce energy consumption and GHG emission of buildings by developing an automated swarm of robots that (i) navigates in buildings without pre-programmed complex localization systems (ii) identifies defects in the building system (e.g., thermal leaks from HVAC) and façades (e.g., cracks, spalling), (iii) generates a precise map of the building, and (iv) supports proactive decision making and reduces material replacement costs for operations and maintenance. We will work closely with industry partners from the public (e.g., City of Vancouver) and private sectors (e.g., SHAPE Management) to identify their real-world challenges, shape the research directions, and achieve knowledge mobilization across a diverse portfolio of stakeholders.

建筑物占加拿大能源消耗的25%，温室气体排放的13%。建筑系统的缺陷（例如，供暖、通风和空调中的泄漏和堵塞; HVAC）和立面（例如，热桥）可导致高达30%的能量损失。此外，诸如管道破裂的构造缺陷可导致有害污染物（例如，玻璃纤维）进入开放空间，对居住者的健康产生负面影响。为了减少建筑行业的环境后果，建筑业主、设施管理者和市政当局正在寻求具有成本和时间效益的方法来（1）检测建筑系统和立面的缺陷;（2）使用主动维护方法来减少能源消耗和温室气体排放。 我们组建了一支由早期职业研究人员组成的跨学科团队，他们在机器人设计和控制、建筑系统优化、生命周期评估、可持续基础设施管理和人类神经科学方面拥有专业知识，以改进当前的建筑设施管理流程。我们将通过柔性机器人的输入和直观的人机交互来重新想象碳中性建筑的未来。我们的长期目标是通过开发自动化机器人群来减少建筑物的能源消耗和温室气体排放，这些机器人（i）在没有 预编程的复杂定位系统（ii）识别建筑系统中的缺陷（例如，来自HVAC的热泄漏）和立面（例如，裂缝、剥落），（iii）生成建筑物的精确地图，以及（iv）支持前瞻性决策并降低运营和维护的材料更换成本。 我们将与来自公众的行业合作伙伴密切合作（例如，温哥华市）和私营部门（例如，SHAPE管理），以确定他们的现实世界的挑战，塑造研究方向，并实现知识动员跨利益相关者的多元化组合。