Unmanned aerial vehicle (UAV) is in an important period of rapid technological breakthrough and application growth, which will vigorously promote the low-altitude economy development. The low-altitude Internet of Intelligences (IoI) is the cornerstone of the UAV industry development and an important infrastructure to realize the "human-UVA-physical objects" fusion and AIoT (All in Internet of Thing) in low-altitude airspace. The construction of low-altitude IoI aims to realize the transition from the traditional Internet to the AIoT by the space-air-ground-sea network infrastructure, and to form a physical cyberspace for the digital and intelligent operation of low-altitude services. It can provide a digital, intelligent, and networked environment for the UAV industry, which is of great significance for promoting the development of low-altitude economy.
The conflict between the thriving drone flight demand and the limited low-altitude airspace has become increasingly apparent. Countries of the world are advancing the research on unmanned aerial vehicle (UAV) regulation policies and technical methods. Even in the U.S. Class G airspace that has the least restrictions, the authorities are considering implementing strict UAV regulation. The UAV cloud management system is a new type of digital supervision method, and its framework and technical roadmap have received extensive attention and research from Chinese and international researchers and regulatory authorities. As one of the applications of cloud management system, geographic information technology is specifically advantaged in the following aspects: Using the global navigation satellite system (GNSS) to achieve precise spatial positioning of UAVs; Using remote sensing (RS) technology to obtain information on geographic constraints that affect the flight safety of UAVs; Organize low-altitude geospatial data based on geographic information system (GIS) and construct low-altitude virtual geographic environment, and so on. This article combines the research progress of our team on low-altitude applications of UAVs and points out that geographic information technology can provide solutions for UAV operation management.
The remote sensing data from unmanned aerial vehicle (UAV) networking refer to the remote sensing observation data obtained under the condition of UAV networking. With the increasing complexity of the UAV networking system, the acquired remote sensing data have grown geometrically, which means that it is urgent to construct a standardized UAV networking remote sensing data management architecture matching the UAV networking technology. Unfortunately, there exists no integrated remote sensing data architecture of UAV networking globally. There is also a lack of literature to systematically introduce the architecture of remote sensing data from UAV networking. This study elaborated systematically the development history of remote sensing data from UAV networking, characteristics of the data, data definition and classification standards, acquisition and processing procedures, and different application scenarios. Finally, a preliminary proposal for the architecture construction of remote sensing data from UAV networking is presented. The results of this study may be useful for the exploration of constructing UAV networking remote sensing data architecture, and provide some references for the formulation of relevant industrial standards for UAV networking, as well as the in-depth application of UAV networking remote sensing data.
The random failure of the unmanned aerial vehicles (UAVs) in performing the task of remote sensing will result in the lack of timely response in the mission execution process, affecting the overall result of the mission or causing partial or complete failure of a mission. To address the failure issues, a redundant and fault-tolerant method for UAV remote sensing networking were proposed. With this method, the lead-follower drone group flight mode is used. Multiple networked drones can complete the designated flight tasks stably and reliably. The simulation result verifies the efficacy of the redundant fault-tolerant method, which can help solve the problem of missing data or task failure in the remote sensing of UAV.
With the rapid development of civilian unmanned aircraft system (UAS) in the fields of logistics and distribution, geographic information detection, and emergency rescue, the U.S. Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA) have jointly developed the Unmanned Aircraft System Traffic Management System (UTM) and carried out a large number of verification tests. According to the technical level, NASA divides the operation technologies and related flight demonstration tests into four technical level phases, of which the TCL-3 and TCL-4 are the core phases of the UTM test and also the most technically complex phases. This article summarized the third and fourth phases of the flight demonstration tests of the UTM system in the United States. Based on the key technologies, the test contents and operation scenarios, as well as the relevant flight experience were summarized. Finally, some recommendations for UTM system design in China were put forward.