Unmanned Aerial Systems (UAS) operations are evolving towards autonomous flight beyond visual line of sight (BVLOS), which requires moving assessments normally conducted by the remote pilot to the autonomous software. An important assessment currently conducted by the remote pilot is whether the current weather conditions pose a safety risk to the flight. This work deals with the development of a software framework for analysing weather data. The developed framework is capable of providing a weather analysis report to a remote supervisor or to an autonomous decision-making software. The software framework has been tested using historical weather data for a full calendar year provided by IBM. The weather data was applied to four different UAS ranging from small UAS to a passenger UAS. The results obtained show that during a full year, flight was possible from 53.9% to 95.8% of the time depending on the UAS. We consider the software framework an important step towards improving the operational safety for autonomous UAS operations under BVLOS conditions.
|Title of host publication||Proceedings of the 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019|
|Publication date||14. Jun 2019|
|Publication status||Published - 14. Jun 2019|
|Event||2019 International Conference on Unmanned Aircraft Systems - Atlanta Marriott Buckhead Hotel & Conference Center, Atlanta, United States|
Duration: 11. Jun 2019 → 14. Jun 2019
Conference number: 229
|Conference||2019 International Conference on Unmanned Aircraft Systems|
|Location||Atlanta Marriott Buckhead Hotel & Conference Center|
|Period||11/06/2019 → 14/06/2019|
- Autonomy, Risk Analysis, Technology Challenges
- Autonomous operations
- UAS safety.
- Weather analysis
- Weather conditions
Lundby, T., Christiansen, M. P., & Jensen, K. (2019). Towards a Weather Analysis Software Framework to Improve UAS Operational Safety. In Proceedings of the 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019 (pp. 1372-1380).  IEEE. https://doi.org/10.1109/ICUAS.2019.8798271