Marc D. Compere, Kevin A. Adkins, Avinash Muthu Krishnan, Ronny Schroeder and Curtis N. James
{"title":"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","authors":"Marc D. Compere, Kevin A. Adkins, Avinash Muthu Krishnan, Ronny Schroeder and Curtis N. James","doi":"10.1039/D2EA00106C","DOIUrl":null,"url":null,"abstract":"<p >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, <img>, 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.</p>","PeriodicalId":72942,"journal":{"name":"Environmental science: atmospheres","volume":" 2","pages":" 214-232"},"PeriodicalIF":2.8000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/ea/d2ea00106c?page=search","citationCount":"0","resultStr":"{\"title\":\"The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors\",\"authors\":\"Marc D. Compere, Kevin A. Adkins, Avinash Muthu Krishnan, Ronny Schroeder and Curtis N. James\",\"doi\":\"10.1039/D2EA00106C\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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, <img>, 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.</p>\",\"PeriodicalId\":72942,\"journal\":{\"name\":\"Environmental science: atmospheres\",\"volume\":\" 2\",\"pages\":\" 214-232\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.rsc.org/en/content/articlepdf/2024/ea/d2ea00106c?page=search\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental science: atmospheres\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2024/ea/d2ea00106c\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental science: atmospheres","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/ea/d2ea00106c","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
The mobility virtual environment (MoVE): an open source framework for gathering and visualizing atmospheric observations using multiple vehicle-based sensors
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.