Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, PA, USA.
Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, USA.
Sci Data. 2021 Jun 18;8(1):155. doi: 10.1038/s41597-021-00930-x.
We autonomously directed a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, were varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.
我们自主指挥了一个小型四旋翼包裹投递无人机 (UAV) 或“无人机”起飞、按照指定路线飞行并降落，总共进行了 209 次飞行，同时改变了一组操作参数。该车辆配备了车载传感器，包括 GPS、IMU、电压和电流传感器以及超声波风速计，以收集关于惯性状态、风速和功耗的高分辨率数据。每次飞行都改变了命令地面速度、有效载荷和巡航高度等操作参数。这个大数据集的总飞行时间为 10 小时 45 分钟，于 2019 年 4 月至 10 月采集，总距离约为 65 公里。通过比较具有相似操作参数的飞行，对收集的数据进行了验证。我们相信这些数据将引起研究和工业界的极大兴趣，他们可以使用这些数据来改进无人机设计、安全性和能源效率，并推进包裹投递无人机飞行操作的物理理解。
