Fully Autonomous Fire Safety Equipment Inspection Missions on a Legged Robot

Abstract

As building automation becomes more prevalent, smart buildings will be integrated into smart cities. Using artificial intelligence (AI) is expected to increase efficiency and the automation level throughout the whole life cycle of building information modeling (BIM). Especially for the management phase of a building, facility managers are recognizing the value of machine learning and robotics for the automation of different maintenance tasks. In the fire safety management field, documentation of fire safety equipment (FSE) is required due to recurring maintenance work, system changes, and relocations. This study concentrates on the automatic detection of inspection tags on FSE using a YOLO network and its deployment on a mobile robot. The goal is to create a fully autonomous inspection mission for a legged robot by executing predefined routes, identifying FSE, and extracting necessary information, such as the next maintenance date tags.