Spatial Mapping of Thermal Comfort for Data-Driven Indoor Spaces Design

Modern building renovations strategies focus on reusing vacant spaces, highlighting the need to optimize these environments using Indoor Environmental Quality (IEQ) data. However, current autonomous monitoring systems still lack a clear integration between robotic spatial mapping and human-centric comfort metrics. To address this gap, this study proposes a novel framework to map indoor comfort conditions, aiming to support data-driven design, management, and reconfiguration of indoor environments. The methodology integrates continuous measurement from stationary environmental sensors with the autonomous navigation and 2D metric mapping capabilities of a mobile social robot. An experimental campaign was conducted in a university office for three months. Environmental data were utilized to compute the simplified Predicted Mean Vote (sPMV) and evaluate both the period-specific Thermal Autonomy (TA) and the Spatial Thermal Autonomy (sTA), quantifying the percentage of time and floor area maintaining optimal comfort. By overlaying these temporal metrics onto the robot-generated spatial map, the system successfully characterized the distinct thermal profiles of the monitored zones. The results revealed significant intra-day thermal variations and localized overheating, demonstrating how spatially resolved comfort maps can provide actionable insights to guide occupant distribution, space optimization, and facility management.