Researchers from Aarhus University have published a paper on the information needs for the autonomous guidance of uncrewed aerial systems (UAS) in U-space, which they say is attracting “significant attention” from the US Federal Aviation Administration.
In a LinkedIn post, researcher and PhD candidate and co-author of the paper, Ivan Panov said the FAA recognised the paper’s relevance to the field of autonomous UAS traffic management and has invited him to present the team’s findings in the coming months. Panov also shared that the FAA will be archiving the paper in its dedicated research repository.
The publication, “A Critical Review of Information Provision for U-Space Traffic Autonomous Guidance”, identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on their study of robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, artificial intelligence, social expectations in Europe on drones, and other factors, the team analysed the existing constraints and the information needs that are of essential importance to address the identified constraints. They then identified critical gaps between the needs and proposed services and classified these gaps in order of priority.
Ultimately, the researchers determined that the present concept of U-space does not satisfy essential information needs for U-space traffic autonomous guidance.
They therefore propose a high-level methodology to identify, measure, and close the gaps identified in their research. This includes developing a scale to measure the information needs for each service specifically. For example, the researchers note that “information required on angles during flight may significantly vary from one approach to 4D trajectory planning to another”. The team proposes that each type of mission be “modelled and tested in simulations and experimental flights, and based on that, the acceptable level of the quality of the information can be found”.
Closing the gaps
To close the identified gaps, the researchers suggest various approaches. For example, “UAS performance, manoeuvrability, and UAS wake vortex category data can be collected as part of an obligatory UAS certification process. We suggest classical approaches such as wind turbine tests, ordinary flight tests, or numerical computation methods. By collecting more data on UAV characteristics, it will be possible to use machine learning techniques for the quick prediction of the tested parameters.”
In addition, the paper proposes that available onboard energy data can be collected by the U-space via cellular networks (4G/5G), Wi-Fi, very high radio frequency, ultra-high frequency bands, or even microwave frequencies. “Optical or laser communication has the potential to transfer data via laser beams with an advanced level of security, as it is hard to intercept the signal. Finally, a recent light fidelity (Li-Fi) technology can be added to the list, as it promises high-speed communication.”
Other proposals to address the gaps include a natural turbulence map and collecting drone user preferences in 4D trajectory planning via an online software interface.
“On-surface dynamic obstacles, airspace intruders, and wildlife data can be collected with various on-board and on-surface sensors and then classified with machine learning techniques,” the paper continues. “Among the potential solutions are GPS and GNSS systems, lidar, radar, infrared and thermal cameras, and optical and video cameras..”
While the researchers say that information about known and unknown environments can be collected and updated with on-board and on-surface sensors, they acknowledge that a specific study on how to fuse multiple-source information in a constantly updated map will be needed.
The status of airworthiness and runway surface conditions should be the area of responsibility of the vertiports, the researchers say, adding that the vertiports should inform the UTM system of the corresponding issues via a software interface. Meanwhile, “UTM security breakthrough status and level of threat can be analysed with software solutions, where AI can play a significant role in identifying atypical activities that correspond with security breakthroughs”.
The paper also proposes that the location of suitable landing areas in case of emergency can be collected with a specific study and updated regularly.
In closing, the researchers say that the identified gaps in information provision must be closed to allow U-space traffic autonomous guidance. The team recommends “a large-scale experiment, including dozens/hundreds of UASs for testing promising technologies and systems via simulations, mathematical models, and flight experiments”.
For more information
Paper: A Critical Review of Information Provision for U-Space Traffic Autonomous Guidance
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