Mobile Robot based Personalized Thermal Comfort Scheme

Indoor thermal control is a crucial technique for ensuring user comfort and efficient energy usage, and typically relies on conventional methods that standardize the indoor thermal environment, neglecting individual personalized preferences. Mobile robots have emerged as a potential solution for personalizing temperature comfort. However, the existing research often falls short in considering factors like robot movement control, user activities, human states, and potential disturbance to users, leading to inaccurate estimations of a user's temperature adjustment needs. This paper introduces a mobile robot-based personalized thermal control system, designed to enhance the accuracy in recognizing human states relevant to thermal comfort in real indoor environments. This system achieves accurate thermal comfort estimation using vision-based recognition while reducing robot movement to decrease user inconvenience. The robot dynamically navigates to optimal positions, guided by the confidence level in vision-based human state recognition. We train the robot's movement control policy using a Deep Reinforcement Learning-based model. Real-world evaluation shows the system's success in accurately recognizing human states with minimal movement trajectories and reduced user discomfort. The proposed robot-based approach offers a significant advancement in personalized thermal control, allowing for more accurate thermal comfort estimation.