EASEEbot: A Robotic Envelope Assessment for Energy Efficiency

Abstract

—Building envelope inspections are necessary to maintain buildings’ energy efficiency, but current solutions are expensive, time-consuming, and destructive. Furthermore, inspectors often face safety and accessibility issues. To mitigate these issues, we propose a holistic system, EASEEbot, consisting of robots to capture data and help retrofit and employ artificial intelligence to assist in data analysis. The robots including an unmanned aerial system (UAS) and ground-penetrating radar (GPR) accommodate data collection while the Robo- dog offers guidance to inspectors in retrofitting phase. The machine learning algorithm helps to analyze the captured data, identifies envelope issues, and generates a building’s digital twin to map identified defects spatially to buildings’ fac¸ades. The retrofit Robo-Dog uses the generated digital twin to project previously recorded defect imagery onto corresponding areas of the building’s envelope. It further guides workers to ensure the identified defective areas are addressed. EASEEbot offers non- destructive sensing, risk mitigation, and high-quality building envelope inspections.