Unmanned Aerial Vehicles (UAVs) are increasingly used in complex civil missions that require reliable operation under uncertainty, creating a need for formal methods to assess how artificial intelligence (AI) contributes to mission performance. This study develops and evaluates a unified modelling framework for AI-enabled UAV systems operating in autonomous and automatic modes on small- and medium-class platforms across different operational configurations, including both single-UAV and multi-UAV deployments. The framework combines a structured decomposition of mission tasks—Environmental Sensing and Monitoring, Situational Awareness, Communication and Sensing Interference Resilience, Hazard and Restricted-Zone Avoidance, and Mission Execution and Intervention—with binary set descriptions, Bayesian Networks (BN), and Reliability Block Diagrams (RBD). This integration enables consistent mapping between mission tasks, AI utilisation approaches, and system-level performance characteristics while accounting for environmental disturbances, communication degradation, and mission constraints. The results show that the framework supports scenario-based analytical evaluation of UAV effectiveness and enables assessment of how AI-enabled perception-stage performance influences mission-level success in a civil Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNe) environment. The proposed framework provides a methodological basis for the design, analysis, and future experimental validation of AI-enabled UAV systems for safety-critical civil missions.
Illiashenko et al. (Mon,) studied this question.