TERRA keeps an ear to the ground on drone integration

Mar. 26, 2019

Partners in the SESAR U-space project, TERRA, held their third consecutive workshop at the Netherlands Aerospace Centre NLR in Amsterdam. Experts in the field of drone operations, regulation, and manufacturing, as well as telecommunication and CNS were invited to discuss the findings of the TERRA project and provide feedback on the technologies analysed. The TERRA project is focused on ground systems, identifying ground technologies and will propose a technical ground architecture to support drone operations.

This was the third in a range of workshops. The prior installments helped the TERRA consortium define unmanned aerial system (UAS) requirements - in terms of ground systems, harmonisation, performance and functionality - and define three business cases for further analysis (Agriculture, Infrastructure inspection and Urban delivery). Through the work of the TERRA project team, new and existing technologies have been identified and analysed. Together with the requirements, a gap analysis was performed to select the most suitable technology for the use-case. The results of this analysis provided the core for the discussion in this workshop. The outcome of the workshop will be used to propose a technical ground-architecture for U-space.

Participants discussed several key elements in drone management: computing technologies for drone traffic management (DTM) System; air-ground communications; navigation technologies; surveillance technologies; and ground-ground DTM communications. A qualitative evaluation was performed for all the presented technologies using a set of performance characteristics, together with an assessment of their pros and cons for drone operations. In addition, a short overview of the activities concerning the use of machine learning (ML) was provided. Machine Learning is seen as a possible technology that can aid in the monitoring of nominal very-low-level (VLL) UAS operations, as well as early detection of off-nominal conditions, such as trajectory deviations. While still in its infancy the results that were obtained and presented were promising.

This project has received funding from the SESAR Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No 763831.