Demo Abstract: A Sensorless Drone-based System for Mapping Indoor 3D Airflow Gradients
20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022
; : 634-635, 2022.
Article
in English
| Scopus | ID: covidwho-1950289
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. © 2022 Owner/Author.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022
Year:
2022
Document Type:
Article
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