Workplace occupant comfort monitoring with a multi-sensory and portable autonomous robot

Abstract The majority of the techniques in the building comfort monitoring state-of-the-art are based on local/manual measurements or on permanent sensor networks. These techniques entail imprecision and randomness (in the first case) and high-cost installations and a lack of flexibility to eventual changes in buildings (in the second). However, intelligent mobile platforms are becoming significantly more important as they perform data acquisition adapted to specific scenarios and schedules. In this paper, we present a robotic platform focused on performing the workplace occupant comfort-monitoring process. The soundness of our proposal compared to others lies on that it gathers most of the necessary properties of an effective monitoring platform: it collects a wider range of variables; it autonomously navigates in inhabited buildings managing occlusions and unexpected events; it conducts multiple monitoring sessions in one or several days; it provides comfort evaluations. Additionally, it can be very useful for energy engineers and construction professional as it provides valuable information in regard to comfort: it detects the best/worst results of the tested variables, locates discomfort in specific areas and moments, recognizes discomfort patterns and globally classifies zones into comfort classes. This robotic platform has been successfully tested in the interiors of buildings, providing significant and clear results in comfort terms (a case study is presented in this paper). However, some limitations and improvements should be addressed. Among other aspects, the computer-robot communication robustness for long distances and the procedure for detecting of small obstacles must be improved in the future.

[1]  Adnan Al-Anbuky,et al.  Development of Intelligent Wireless Sensor Networks for Human Comfort Index Measurement , 2011, ANT/MobiWIS.

[2]  M. Arif,et al.  Effect of thermal comfort on occupant productivity in office buildings: Response surface analysis , 2020, Building and Environment.

[3]  S. Karjalainen Gender differences in thermal comfort and use of thermostats in everyday thermal environments , 2007 .

[4]  Wai Lok Chan,et al.  A distributed sensor network for measurement of human thermal comfort feelings , 2008 .

[5]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[6]  B. Lennox,et al.  A sensor network for predicting and maintaining occupant comfort , 2013, 2013 IEEE Workshop on Environmental Energy and Structural Monitoring Systems.

[7]  Antonio Barrientos,et al.  Heterogeneous Multi-Robot System for Mapping Environmental Variables of Greenhouses , 2016, Sensors.

[8]  Young Lee Workplace Health and Its Impact on Human Capital: Seven Key Performance Indicators of Workplace Health , 2019, Indoor Environment and Health.

[9]  Swades De,et al.  RF energy harvester-based wake-up receiver , 2015, 2015 IEEE SENSORS.

[10]  Eduard Clotet,et al.  Application of an Array of Metal-Oxide Semiconductor Gas Sensors in an Assistant Personal Robot for Early Gas Leak Detection , 2019, Sensors.

[11]  Torsten Bertram,et al.  Integrated online trajectory planning and optimization in distinctive topologies , 2017, Robotics Auton. Syst..

[12]  Murat Demirbas,et al.  Wireless Sensor Networks for Monitoring of Large Public Buildings , 2005 .

[13]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[14]  Ming Jin,et al.  Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring , 2018 .

[15]  Carol C. Menassa,et al.  Ambient data collection in indoor building environments using mobile robots , 2016 .

[16]  W. H. Engelmann,et al.  The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[17]  Gianpaolo Vitale,et al.  Explainable Post-Occupancy Evaluation Using a Humanoid Robot , 2020, Applied Sciences.

[18]  Fergus Nicol,et al.  Post-occupancy evaluation and field studies of thermal comfort , 2005 .

[19]  Burcin Becerik-Gerber,et al.  Continuous Sensing of Occupant Perception of Indoor Ambient Factors , 2011 .

[20]  Wouter D. van Marken Lichtenbelt,et al.  Energy consumption in buildings and female thermal demand , 2015 .

[21]  Carol C. Menassa,et al.  A taxonomy of data types and data collection methods for building energy monitoring and performance simulation , 2016 .

[22]  Martin Tenpierik,et al.  A review into thermal comfort in buildings , 2013 .

[23]  Carol C. Menassa,et al.  Robotic data collection and simulation for evaluation of building retrofit performance , 2018, Automation in Construction.

[24]  Bing Chen,et al.  Design of Building Environment Mobile Monitoring and Safety Early Warning Robot , 2018, ICSCIB.

[25]  Julia Armesto,et al.  Indoor Multi-Sensor Acquisition System for Projects on Energy Renovation of Buildings , 2016, Sensors.

[26]  Eduard Clotet,et al.  Automatic Supervision of Temperature, Humidity, and Luminance with an Assistant Personal Robot , 2017, J. Sensors.

[27]  Dani Martínez,et al.  Ambient Intelligence Application Based on Environmental Measurements Performed with an Assistant Mobile Robot , 2014, Sensors.

[28]  Carol C. Menassa,et al.  Real-time building energy and comfort parameter data collection using mobile indoor robots , 2015 .

[29]  David Faulkner,et al.  Control of temperature for health and productivity in offices , 2004 .