Issues of assessing the feasibility of tasks assigned to unmanned aerial vehicles


DOI: 10.34759/trd-2022-127-20

Аuthors

Javadov N. H.1*, Agayev F. G.2**, Huseynov H. A.3***, Zulfugarli P. R.3****

1. National Aerospace Agency of Azerbaijan Republic, NASA, 1, Suleyman Sani Akhundov str., Baku, AZ1115, Azerbaijan Republic
2. Institute for Space Research of Natural Resources National Aerospace Agency, 1, S.S. Akhundov str., Baku, AZ1106, Azerbaijan
3. Azerbaijan Technical University, 25, Hussein Javid prosp., Baku, 370073, Azerbaijan

*e-mail: Anasa@yahoo.com
**e-mail: agayev.tekti@mail.ru
***e-mail: tk_xt2001@mail.ru
****e-mail: Peri.rzayeva30@gmail.com

Abstract

The article deals with the feasibility assessing of the tasks assigned to unmanned aerial vehicles (UAV), and presents a method for the quality assessment of the military purpose UAV. As the result of the said assessment, the resulting inference with account for the unmanned engineering requirements and preferences is being formed. Criteria, which include indicators such as conformity, capability, operational security, sustainability and readiness for solving the tasks assigned to the UAV are known as well. In this regard, it is especially important to address the issues of the used sensors capabilities matching to solve the tasks assigned to the UAV. This task is usually being solved by modeling. Besides, the descriptive structure of a special metric is employed as well to develop a criterion for the UAV tasks feasibility assessing. For example, the issue of the of UAV tasks feasibility while searching for some objects depending on the terrain real landscape should be solved by creating a special methodology for the terrain visualization assessing depending on the state of the surface relief under study. Solving the issue of tasks feasibility is closely associated with the issue of a rational compromise achieving between the UAV total load, various sensors and the UAV mission feasibility. The article studied the issue on the tasks feasibility assigned to the UAV by solving the problem of developing the new criterion of executing functions of objects detection on the surface of the sought-for site by the UAV. Based on the well-known empirical criterion of the said task feasibility, a new indicator has been formed as a logarithm of the ratio of the task fulfillment and nonfulfillment probabilities. Based on the proposed indicator, the invariant linking this indicator with the number of work cycles, which ensure the corresponding probabilities of the task fulfillment, has been formed.

Keywords:

unmanned aerial vehicles, object detection, mission probability, visual environment, criterion

References

  1. Mavris D.N., DeLaurentis D.A. An integrated approach to military aircraft selection and concept evaluation, 1st AIAA Aircraft Engineering, Technology and Operations Congress, Los Angeles, CA, USA, 1995 URL: https://www.researchgate.net/publication/27523135. DOI: 10.2514/6.1995-3921
  2. Preece A., Gomez M., de Mel G., Vasconcelos W., Sleeman D., Colley S., Pearson G., Pham T. and Porta T. Matching sensors to missions using a knowledge-based approach, Proceedings of SPIE — The International Society for Optical Engineering, May 2008. URL: https://www.researchgate.net/publication/228459757_Matching_sensors_to_missions_using_a_knowledge-based_approach. DOI:10.1117/12.782648
  3. Preece A., Gomez M., de Mel G., Colley S., La Porta T. An ontology-based approach to sensor-mission assignment, First Conference of the International Technology Alliance on Networks and Information Processing (ACITA), Baltimore, Maryland, USA. Alliance, 2007. URL: https://www.researchgate.net/publication/252452097
  4. Gomez M., Preece A., Johnson M., de Mel G., Vasconcelos W., Gibson C., Bar-Noy A., Borowiecki K., Porta T. and Pizzocaro D. An ontology-centric approach to sensor-mission assignment, 16th International Conference on Knowledge Engineering and Knowledge Management, EKAW-2008, 2008, pp. 347–363. URL: https://doi.org/10.1007/978-3-540-87696-0_30
  5. Göktogan A.H. et al. Airborne vision sensor detection performance simulation, Simulation Conference and Exhibition (SimTecT’05), Sydney, Australia, 2005. URL: https://www.researchgate.net/publication/228994396_Airborne_vision_sensor_detection_performance_simulation
  6. Morawietz S., Strohal M., Stürz P. Consideration of surveillance sensor capabilities within the holistic evaluation of aerial platforms, Deutscher Luft- und Raumfahrtkongress, Conference Paper, 2016.
  7. Pavlov A.N., Pavlov D.A., Umarov A.B. Trudy MAI, 2021, no. 120. URL: https://trudymai.ru/eng/published.php?ID=161425. DOI: 10.34759/trd-2021-120-11
  8. Popov E.P., Vereikin A.A., Nasonov F.A. Trudy MAI, 2021, no. 120. URL: https://trudymai.ru/eng/published.php?ID=161429. DOI:10.34759/trd-2021-120-15
  9. Kalyagin M.Yu., Voloshin D.A., Mazaev A.S. Trudy MAI, 2020, no. 112. URL: https://trudymai.ru/eng/published.php?ID=116625. DOI:10.34759/trd-2020-112-20
  10. Zul’fugaply P.R. Trudy MAI, 2021, no. 117. URL: https://trudymai.ru/eng/published.php?ID=156319. DOI:10.34759/trd-2021-117-17
  11. Agaev F.G., Asadov Kh.G., Aslanova A.B. Trudy MAI, 2021, no. 117. URL: https://trudymai.ru/eng/published.php?ID=156313. DOI: 10.34759/trd-2021-117-16
  12. Dmitriev V.I., Zvonarev V.V., Lisitsyn Yu.E. Trudy MAI, 2020, no. 112. URL: https://trudymai.ru/eng/published.php?ID=116566. DOI:10.34759/trd-2020-112-16
  13. Podstrigaev A.S., Slobodyan M.G., Mozhaeva E.I. Trudy MAI, 2019, no. 106. URL: https://trudymai.ru/eng/published.php?ID=105716
  14. Nuriev M.G. Trudy MAI, 2018, no. 102. URL: https://trudymai.ru/eng/published.php?ID=99074
  15. Fokina E., Feger J., Hornung M. Application of a visualization environment for the mission performance evaluation of civilian UAS, CEAS Aeronautical Journal, 2019, vol. 10, pp. 817–825. DOI:10.1007/s13272-018-0350-z
  16. Stecz W., Gromada K. UAV Mission Planning with SAR Application, Sensors, 2020, vol. 20(4), pp. 1080. DOI:10.3390/s20041080
  17. Fokina E., Feger J., Hornung M. An integrated UAS design optimization based on mission assessment and evaluation, Deutscher Luft- und Raumfahrtkongress, 2018. DocumentID: 480148.
  18. Langer H. Extended Evolutionary Algorithms for Multiobjective and Discrete Design Optimization of Structures, Dissertation, Technical University of Munich, Munich, Germany, 2005.
  19. Ekaterina Fokina, Jens Feger, Mirko Hornung. A Missions Performance Evaluation Approach for Civil UAS Applications, MATEC Web of Conferences, 2018, vol. 221. URL: https://doi.org/10.1051/matecconf/201822105006
  20. Johnson J. Analysis of image forming systems, Proceedings of Image Intensifier Symposium, Ft. Belvoir, Virginia, 1958, pp. 249-273.

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