New innovations bolster American workforce & advance building envelope retrofits

A team of students in the labs of civil engineering Professors Chen Feng and Semiha Ergan are finalists in the U.S. Department of Energy’s Envelope Retrofit Opportunities for Building Optimization Technologies (E-ROBOT) Prize. EASEEBot is an AI equipped drone-based building inspection system that can find defects in structures using digital and thermal cameras. 

The U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) announced ten winners for the first phase of the competition, whose goal is to combine advanced retrofit capabilities with collaborative robotics to improve the safety and productivity of the American buildings workforce.

EASEEBot, develped by Daniel Lu, Xuchu Xu, Bilal Sher, Sunglyoung Kim, Abhishek Rathod, in Ergan and Feng's  BiLab and AI4CE lab, respectively, is an AI equipped drone-based building inspection system that can find defects in structures using digital and thermal cameras. 

Annually, the United States spends over $42 billion on leak and moisture related energy costs; the leaks are hard to find, and the current processes to locate them are intrusive, expensive, and a hazard to worker safety. EASEEBot’s two modes, fly and climb, paired with its digital and thermal cameras, allows the robot to identify air leaks, defects, and intrusions. This robot can work in confined spaces, allowing workers to stay out of hazardous areas.