To the risk management methodology of unmanned aircraft systems

Operators of unmanned aircraft systems (UAS), performing aeronautical work in accordance with the certification requirements of the Federal Aviation Regulations, are obliged to have a Flight Safety Management System (FSMS), the critical element of which is risk management. The utilization of the risk assessment methods and the development of corrective actions, used in manned aviation, is problematic due to the UAS operational characteristics. The article presents the FSMS methodology for UAS of category B (special category) based on the developments of JARUS group, established with EASA, to address the unmanned aviation operation issues. The proposed methodology integrates the SORA method provisions, developed by JARUS, and comprised into seven documents, into the unified logically related system with consideration to the specific traits of the Russian Federation Aviation legislation and the terminology adopted in the Russian Federation civil aviation. The key problem of assessing the expected effectiveness of risk management actions in the JARUS methodological guidelines does not have a complete solution.

The article proposes to solve the problem using professional evaluation by means of the hierarchy analysis method of the fuzzy set theory. This allows us to reasonably build the hierarchy of actions according to their impact on risk mitigation and to classify them into three categories. Such a classification is imperative to evaluate the applicability of actions depending on the amount of total risk. Although the UAS operation of category B is supposed to be performed in the segregated airspace, the methodology takes into consideration not only the risks of collision with objects on the ground but also mid-air collision risks with manned aircraft, since it is usually impractical to create the “ideal” segregated airspace. The proposed methodology can be utilized in the long view when developing risk management practices of UAS operation of category C in open skies.

1. Prosvirina, N.V. (2021). Analysis and prospects for the development of unmanned aircraft. Moskovskiy ekonomicheskiy jurnal, no. 10, pp. 560–575. DOI: 10.24412/2413-046Х2021-10619 (in Russian)

2. Xia, J., Wang, K. & Wang, S. (2019). Drone scheduling to monitor vessels in emission control areas. Transportation Research Part B: Methodological, vol. 119, pp. 174–196. DOI: 10.1016/j.trb.2018.10.011

3. Bhosale, S., Shelar, S., Anadkar, V., Pawar, S. & Sarange, S. (2019). Air Ambulance Drone (UAV). International Research Journal of Engineering and Technology, no. 6, pp. 152–154.

4. Wild, G., Murray, J. & Baxter, G. (2016). Exploring civil drone accidents and incidents to help prevent potential air disasters. Aerospace, vol. 3, issue 3, 22 p. DOI: HYPERLINK "http://dx.doi.org/10.3390/aerospace 3030022"10.3390/aerospace3030022 (accessed: 27.01.2022).

5. Sharov, V.D., Eliseev, B.P. & Polyakov, P.M. (2021). About flight safety management during operation of unmanned aircraft systems. Civil Aviation High Technologies, vol. 24, no. 3, pp. 42–56. DOI: 10.26467/2079-0619- 2021-24-3-42-56 (in Russian)

6. Murzilli, L. (2017). JARUS guidelines on specific operations risk assessment. JARUS. 26.06.2017. Available at: http://jarusrpas.org/sites/jarus-rpas.org/files/jar_doc_06_ jarus_sora_v1.0.pdf (accessed: 27.01.2022).

7. Terkildsen, K.H. & Jensen, K. (2019). Towards a tool for assessing UAS compliance with the JARUS SORA guidelines. 2019 International Conference on Unmanned Aircraft Systems (ICUAS). Atlanta, GA, USA, pp. 460–466. DOI: 10.1109/ICUAS.2019.8798236

8. Pérez-Castána, J.A., Comendadora, F.G., Rodríguez-SanzaI, A., Cabreraa, I.A. & Torrecilla, J. (2019). RPAS conflict-risk assessment in non-segregated airspace. Safety Science, vol. 111, pp. 7–16. DOI: 10.1016/j.ssci.2018.08.018

9. Saati, T.L. (1993). [Decision-making. Method of hierarchy analysis]. Translated from English by R.G. Vachnadze. Moscow: Radio i svyaz, 278 p. (in Russian)

10. Saati, T.L. (2015). On the measurement of intangibles. A principal eigenvector approach to relative measurement derived from paired comparisons. Cloud of Science, vol. 2, no. 1, pp. 5–39. Available at: http://twt.mpei.ac.ru/ ochkov/CoS_2_1.pdf (accessed: 27.01.2022). (in Russian)

11. Bogachenko, N.F. & Lavrov, D.N. (2020). On the features of the implementation of the interval method for analytic hierarchy process in the problem of assessing the effectiveness of the employment service. Mathematical Structures and Modeling, no. 4 (56), pp. 41–48. DOI: 10.24147/2222-8772.2020.4.41-48 (in Russian)

12. Korobov, V.B. & Tutygin, A.G. (2016). Problems of the analytic hierarchy process and some solutions. Economics and management, no. 8 (130), pp. 60–65. (in Russian)

13. Voskobinsky, M.Yu., Pekarskaya, O.A. & Razi, D.A. (2016). [Decision making based on the hierarchy analysis method]. Ekonomika i upravleniye narodnym khozyaystvom, no. 2, pp. 33–42. (in Russian)

14. Nogin, V.D. (2004). [A simplified version of the hierarchy analysis method based on nonlinear criteria convolution]. Zhurnal Vychislitelnoi Matematiki i Matematicheskoi Fiziki, vol. 44, no. 7, pp. 1261–1279. (in Russian)