Abstract
With the global spread of the COVID-19 pandemic, ventilation indoors is becoming increasingly important in preventing the spread of airborne viruses. However, while sensors exist to measure wind speed and airflow gradients, they must be manually held by a human or an autonomous vehicle, robot, or drone that moves around the space to build an airflow map of the environment. In this demonstration, we present DAE, a novel drone-based system that can automatically navigate and estimate air flow in a space without the need of additional sensors attached onto the drone. DAE directly utilizes the flight controller data that all drones use to self-stabilize in the air to estimate airflow. DAE estimates airflow gradients in a room based on how the flight controller adjusts the motors on the drone to compensate external perturbations and air currents, without the need for attaching additional wind or airflow sensors.
Original language | English (US) |
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Title of host publication | MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services |
Publisher | Association for Computing Machinery, Inc |
Pages | 634-635 |
Number of pages | 2 |
ISBN (Electronic) | 9781450391856 |
DOIs | |
State | Published - Jun 27 2022 |
Event | 20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022 - Portland, United States Duration: Jun 27 2022 → Jul 1 2022 |
Publication series
Name | MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services |
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Conference
Conference | 20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022 |
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Country/Territory | United States |
City | Portland |
Period | 6/27/22 → 7/1/22 |
Funding
4 DEMONSTRATION DESCRIPTION In this demonstration, we will show how DAE can map the airflow of a small and blocked off space. The drone will fly around this space, estimating and mapping airflow gradients in real-time. Additionally, we will have items that generate stronger airflows on hand (e.g., hair dryers and vacuums), which we will use to change the airflow of the demonstration area to show how DAE can measures changes in airflows. ACKNOWLEDGMENTS This research was partially supported by the National Science Foundation under Grant Numbers CNS-1704899, CNS-1815274, CNS-11943396, and CNS-1837022. The views and conclusions contained here are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Columbia University, NSF, or the U.S. Government or any of its agencies. REFERENCES [1] A. S. Gillies, H. Wu, N. Tuffs, and T. Sartor. Development of a real time airflow mon-itoring and control system. In Tenth US Mine Ventilation Symposium, Anchorage, Balkema, The Netherlands, pages 145–155, 2004. [2] Y. Li, G. M. Leung, J. Tang, X. Yang, C. Chao, J. Z. Lin, J. Lu, P. V. Nielsen, J. Niu, H. Qian, et al. Role of ventilation in airborne transmission of infectious agents in the built environment-a multidisciplinary systematic review. Indoor air, 17(1):2–18, 2007. [3] R. Mur-Artal and J. D. Tardós. Orb-slam2: An open-source slam system for monoc-ular, stereo, and rgb-d cameras. IEEE transactions on robotics, 33(5):1255–1262, 2017. [4] R. Sato, K. Tanaka, H. Ishida, S. Koguchi, J. Pauline Ramos Ramirez, H. Matsukura, and H. Ishida. Detection of gas drifting near the ground by drone hovering over: Using airflow generated by two connected quadcopters. Sensors, 20(5), 2020. [5] S. Xia, R. Chandrasekaran, Y. Liu, C. Yang, T. S. Rosing, and X. Jiang. A drone-based system for intelligent and autonomous homes. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, SenSys ’21, page 349–350, New York, NY, USA, 2021. Association for Computing Machinery.
Keywords
- artificial intelligence
- edge computing
- pervasive sensing
- public health
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications