Spatial Mapping of Thermal Comfort for Data-Driven Indoor Spaces Design
Year: 2026
Type: conference-paper
Abstract: 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.
Institution: Marche Polytechnic University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204184678