Acquisition and Visualization of HVAC Data by Autonomous Mobile Robots

The monitoring of indoor HVAC data is critical in smart buildings to optimize energy use and maintain thermal comfort. Conventional stationary sensor systems face challenges in capturing data from elevated or diverse spatial points, limiting their ability to provide comprehensive volumetric data. These systems also lack adaptability to dynamic environmental changes and require an impractically dense sensor network for high spatial resolution. This study introduces a mobile robot-based approach for acquiring volumetric air conditioning data, aiming to achieve coverage comparable to densely distributed stationary sensors while enhancing flexibility and reducing infrastructure costs. Equipped with a vertically extending linear actuator, the robot collects airflow and temperature data using a 3D wind sensor and a temperature sensor mounted on the actuator’s tip. To ensure accuracy, the robot’s path was carefully planned to minimize the effects of the sensor’s thermal response time, ensuring consistent and reliable measurements. Using the collected data, a detailed thermal-airflow map of a coworking space was generated. Results demonstrate that this dynamic sensing approach offers significant advantages over stationary systems, presenting a cost-effective and flexible alternative for indoor environmental monitoring.