The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors

IF 2.8 Q3 ENVIRONMENTAL SCIENCES
Marc D. Compere, Kevin A. Adkins, Avinash Muthu Krishnan, Ronny Schroeder and Curtis N. James
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引用次数: 0

Abstract

Uncrewed Aircraft Systems (UAS) are becoming prevalent in a wide variety of meteorological investigations. UAS fill an important atmospheric observational gap, namely observations between ground-based sensors and higher altitudes where manned aircraft can safely operate. This paper explores the hardware and software design used for a multi-vehicle atmospheric data collection campaign. The Mobility Virtual Environment (MoVE) is a software framework designed specifically to collect data from multiple vehicles and present a coherent, summary view of a complex scenario. Using both a 2D map and a live updating table, multiple vehicles can be monitored simultaneously to make real-time decisions and quickly assess the mission's effectiveness. MoVE is the software framework used to gather live telemetry inputs before, during, and after flight. MoVE is also the set of tools used to post-process multiple data logs from days of flight experiments into 3D and 4D visualizations over the surrounding terrain. The results are visualizations of otherwise invisible quantities like T, P, RH, and especially vector wind velocities, , captured during flight with drone-based sensors. The open-source software and procedures described here can help the atmospheric research, and broader scientific community, achieve greater understanding when using drone-based sensors.

Abstract Image

移动虚拟环境(MoVE):利用多个车载传感器收集和可视化大气观测数据的开源框架
无螺旋桨飞机系统(UAS)正在广泛应用于各种气象调查。无人机系统填补了一个重要的大气观测空白,即在地面传感器和有人驾驶飞机可以安全运行的更高海拔之间进行观测。本文探讨了用于多飞行器大气数据收集活动的硬件和软件设计。移动虚拟环境(MoVE)是一个软件框架,专门用于收集多飞行器的数据,并提供复杂场景的连贯、汇总视图。通过使用二维地图和实时更新表格,可以同时监测多辆飞行器,从而做出实时决策并快速评估任务的有效性。MoVE 是用于在飞行前、飞行中和飞行后收集实时遥测输入的软件框架。MoVE 也是一套工具,用于将多日飞行实验的多个数据日志后处理成周围地形的三维和四维可视化图像。其结果是在飞行过程中利用无人机传感器捕捉到的温度、湿度、相对湿度,特别是矢量风速等不可见量的可视化。这里介绍的开源软件和程序可以帮助大气研究和更广泛的科学界在使用无人机传感器时获得更深入的理解。
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CiteScore
2.90
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