ONTOLOGY-DRIVEN SITUATIONAL MODELING OF INTERACTIONS IN UAV SWARMS.
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| Title: | ONTOLOGY-DRIVEN SITUATIONAL MODELING OF INTERACTIONS IN UAV SWARMS. |
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| Alternate Title: | ОНТОЛОГІЧНО-ОРІЄНТОВАНЕ СИТУАЦІЙНЕ МОДЕЛЮВАННЯ ВЗАЄМОДІЙ У РОЯХ БПЛА |
| Authors: | Rohatskyi, I. Y.1 ihor.rohatskyi@lnu.edu.ua, Lyashkevych, V. Y.1 vasyl.liashkevych@lnu.edu.ua |
| Source: | Informatics & Mathematical Methods in Simulation / Informatika ta Matematičnì Metodi v Modelûvannì. 2026, Vol. 16 Issue 2, p237-245. 9p. |
| Subjects: | Autonomous robots, Collective behavior, Aggregation (Robotics), Knowledge representation (Information theory), Contextual analysis, Decision making |
| Abstract: | Drone swarms are of great interest to the scientific community, but there are still many problems in the organization of control in an autonomous drone swarm, among which a special place is occupied by semantic coherence of agent interaction, maintenance of situational awareness, distribution and updating of knowledge, as well as decision-making in conditions of incomplete information, changing context and limited communication. In such conditions, the effectiveness of collective behavior of the swarm is determined not only by the quality of local perception of an individual drone, but also by the ability of agents to interpret situations based on a shared knowledge model. The study proposes an approach to modeling drone swarm interaction based on a situational approach and ontological representation of knowledge. Swarm interaction is formalized as a sequence of typical situations described through a set of semantically consistent entities, including agent, role, task, goal, region, event, risk, constraint, resource, and communication state. A conceptual ontological model is formed, designed to structure knowledge about the mission, environmental context, agent states, coordination rules, and transitions between situations. The proposed approach allows integrating situational modeling with decision support mechanisms, providing a single semantic environment for the interaction. The scientific novelty of the work lies in the combination of a situational approach with ontological modeling for the representation and updating of knowledge in an autonomous drone swarm. This approach creates a basis for the formalization of collective behavior, validation of interaction scenarios, explainability of decisions, and construction of control systems based on ontologies. The practical value of the research lies in the possibility of using the proposed model in simulation environments, in particular Webots, through specially designed integrators. The developed knowledge model was analyzed for scenarios of searching and tracking moving objects on the landscape. The testing results indicate an increase in the coherence of the collective behavior of the swarm, the quality of distributed decision-making, the reproducibility of experiments and the scalability of the system. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | Drone swarms are of great interest to the scientific community, but there are still many problems in the organization of control in an autonomous drone swarm, among which a special place is occupied by semantic coherence of agent interaction, maintenance of situational awareness, distribution and updating of knowledge, as well as decision-making in conditions of incomplete information, changing context and limited communication. In such conditions, the effectiveness of collective behavior of the swarm is determined not only by the quality of local perception of an individual drone, but also by the ability of agents to interpret situations based on a shared knowledge model. The study proposes an approach to modeling drone swarm interaction based on a situational approach and ontological representation of knowledge. Swarm interaction is formalized as a sequence of typical situations described through a set of semantically consistent entities, including agent, role, task, goal, region, event, risk, constraint, resource, and communication state. A conceptual ontological model is formed, designed to structure knowledge about the mission, environmental context, agent states, coordination rules, and transitions between situations. The proposed approach allows integrating situational modeling with decision support mechanisms, providing a single semantic environment for the interaction. The scientific novelty of the work lies in the combination of a situational approach with ontological modeling for the representation and updating of knowledge in an autonomous drone swarm. This approach creates a basis for the formalization of collective behavior, validation of interaction scenarios, explainability of decisions, and construction of control systems based on ontologies. The practical value of the research lies in the possibility of using the proposed model in simulation environments, in particular Webots, through specially designed integrators. The developed knowledge model was analyzed for scenarios of searching and tracking moving objects on the landscape. The testing results indicate an increase in the coherence of the collective behavior of the swarm, the quality of distributed decision-making, the reproducibility of experiments and the scalability of the system. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 22235744 |
| DOI: | 10.15276/imms.v16.no2.237 |
