Mathematical formulation of the ontology-based selection algorithm for fixed-wing unmanned aerial vehicles

Iurii Vorobiov, Kateryna Maiorova, Oleksandr Pidlisnyi

Abstract


The subject of this article is the formalized selection of fixed-wing unmanned aerial vehicles based on integrated ontology modeling, logical inference, and multi-criteria evaluation techniques. The goal of the research is to develop a mathematical formulation of an integrated UAV-selection algorithm that enables logical elimination of infeasible alternatives and subsequent formal ranking of feasible platforms. The tasks are as follows: to formalize the UAV domain knowledge in the form of an ontology; to develop SWRL rules for automatic verification of mission compatibility; to construct mathematical procedures for data normalization and criteria weighting; to define ranking procedures based on TOPSIS; and to demonstrate the application of the algorithm using representative UAV models. The methods used include ontology modeling in OWL2DL; logical inference based on SWRL; multi-criteria methods AHP and TOPSIS; mathematical normalization, weighted-matrix construction, computation of ideal and anti-ideal vectors, and determination of closeness coefficients. The results obtained include the development of an integrated ontology-driven mathematical model that automatically excludes infeasible UAV platforms and performs quantitative evaluation of suitable alternatives. On the example of Kub-UAV, Orlan-10, Orlan-30, and BayraktarTB2, the proposed algorithm demonstrated transparent and reproducible recommendations: BayraktarTB2 achieved the highest rating due to endurance, payload, and sensor configuration; Orlan-10 appeared optimally balanced for tactical missions; Orlan-30 was limited by endurance; Kub-UAV logically ranked last due to its loitering-munition concept. Conclusions. The scientific novelty lies in the development of the first comprehensive model combining ontology-based domain representation, logical filtering, and mathematical ranking in a single integrated framework. The practical value is associated with applicability in military and civilian decision-support systems for reconnaissance, monitoring, mapping, and other mission types. The proposed architecture is scalable and may be extended with additional criteria, fuzzy-logic techniques, and industrial data sources.

Keywords


ontology; UAV; decision support system; AHP; TOPSIS; logical inference

References


Valavanis, K. P. & Vachtsevanos, G. J. Handbook of Unmanned Aerial Vehicles. Dordrecht: Springer, 2015. 3024 p. DOI: 10.1007/978-90-481-9707-1.

Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A. & et al. Unmanned Aerial Vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 2019, vol. 7. pp. 48572-48634. DOI: 10.1109/ACCESS.2019.2909530.

Gómez-Pérez, A., Fernández-López, M. & Corcho, O. Ontological Engineering. London, Springer-Verlag, 2004, 404 p. DOI: 10.1007/b97353.

Gruber, T. R. Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 1995, vol. 43, iss. 5–6, pp. 907–928. DOI: 10.1006/ijhc.1995.1081.

Noy, N. F. & McGuinness, D. L. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford University, 2001. 25 p. Available at: https://protege.stanford.edu/publications/ontology_development/ontology101.pdf (accessed 12.02.2025).

Saaty, T. L. The Analytic Hierarchy Process. New York, McGraw-Hill, 1980. 286 p. Available at: https://books.google.com.ua/books?id=Xxi7AAAAIAAJ&hl=uk&source=gbs_book_other_versions (accessed 12.02.2025).

Hwang, C. L. & Yoon, K. Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey. Berlin: Springer-Verlag, 1981, 269 p. DOI: 10.1007/978-3-642-48318-9.

Sharma, S. K., Singh, V., Puri, S., Rathour, G., et al. A fuzzy multi-criteria decision-making framework for assessing the impact of multi-agent technology in preventing the probabilities of airplane crashing. Aircraft Engineering and Aerospace Technology, 2025, vol. 97, iss.10, pp. 1320-1332. DOI: 10.1108/AEAT-04-2025-0154.

Insaurralde, C. C. & Blasch, E. Ontological airspace-situation awareness for decision system support. Aerospace, 2024, vol. 11, iss. 11, article no. 942. DOI: 10.3390/aerospace11110942.

Martín-Lammerding, D., Astrain, J. J. & Córdoba, A. Dronetology: A domain ontology for UAS applications. Knowledge-Based Systems, 2025, vol. 327, article no. 114074. DOI: 10.1016/j.knosys.2025.114074.

Bai, Z., Zhang, B., Tian, Z., Zou, S., & Zhu, W. Implementing a multi-attribute decision-making-based approach to evaluate small electric vertical takeoff and landing fixed-wing drones with mission efficiency. Aerospace, 2024, vol. 11, iss. 7, article no. 568. DOI: 10.3390/aerospace11070568.

Martín-Lammerding, D., Astrain, J. J., Córdoba, A., & Villadangos, J. E. An ontology-based system to avoid UAS flight conflicts and collisions in dense traffic scenarios. Expert Systems with Applications, 2023, vol. 215, article no. 119027. DOI: 10.1016/j.eswa.2022.119027.




DOI: https://doi.org/10.32620/aktt.2025.6.04