Y. Liu, M. Zhao, S. Xia, E. Wu, X. Jiang
{"title":"Demo Abstract: A Sensorless Drone-based System for Mapping Indoor 3D Airflow Gradients","authors":"Y. Liu, M. Zhao, S. Xia, E. Wu, X. Jiang","doi":"10.1145/3498361.3538671","DOIUrl":"https://doi.org/10.1145/3498361.3538671","url":null,"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.","PeriodicalId":347635,"journal":{"name":"20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122231393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2