Conceptual Design of a Robotic Building Envelope Assessment System for Energy Efficiency

Building air leakage and moisture issues can result in significant energy loss, shorten the building envelope life cycle and require additional maintenance costs. While these issues present risks to a building and its occupants, they could be difficult to detect during building maintenance and early stages of retrofit projects. Currently, mapping air leakage and moisture issues over a building's envelope rely on manual inspections. These methods are intrusive, expensive, and hazardous to inspectors' safety. To address these challenges, we propose a non-invasive and safe conceptual solution, a Robotic Envelope Assessment System for Energy Efficiency (EASEEbot), to locate and document moisture intrusion, thermal bridges, and air leaks. EASEEbot is a high-power wall climbing drone and comes with a multi-function toolbox. It can capture 3D thermal images and auto-generate 3D models using image-based Structure from Motion (SfM) and Visual Simultaneous Localization and Mapping (VSLAM). Our deep learning algorithms will rapidly identify common building envelope defects from multi-modal sensing data. EASEEbot is also designed with a tethered wall-climbing mode and will use long-wave radar to detect hidden trapped interstitial moisture and other major envelope defects.