A sensorless drone-based system for mapping indoor 3D airflow gradients: demo abstract
A sensorless drone-based system for mapping indoor 3D airflow gradients: demo abstract
复制标题
基于无传感器无人机的系统，用于绘制室内 3D 气流梯度：演示摘要
DOI：
10.1145/3498361.3538671
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Jiang, Xiaofan
中科院分区：
文献类型：
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作者：
Liu, Yanchen;Zhao, Minghui;Xia, Stephen;Wu, Eugene;Jiang, Xiaofan
随着COVID-19大流行的全球蔓延，室内通风对于防止空气传播病毒的传播变得越来越重要。然而，虽然存在用于测量风速和气流梯度的传感器，但它们必须由人类或在空间中移动的自动驾驶汽车、机器人或无人机手动控制，以构建环境的气流图。在这个演示中，我们展示了DAE，一种新型的基于无人机的系统，它可以自动导航和估计空间中的气流，而不需要附加在无人机上的额外传感器。DAE直接利用所有无人机在空中自稳定使用的飞行控制器数据来估计气流。DAE根据飞行控制器如何调整无人机上的马达以补偿外部扰动和气流来估计房间内的气流梯度，而无需附加额外的风或气流传感器。
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.
基于无人机的智能和自主家庭系统
DOI：
10.1145/3485730.3492881
发表时间：
2021
期刊：
Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
影响因子：
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作者：
Xia, Stephen;Chandrasekaran, Rishikanth;Liu, Yanchen;Yang, Chenye;Rosing, Tajana Simunic;Jiang, Xiaofan
通讯作者：
Jiang, Xiaofan