A Fermatean fuzzy CRITIC WASPAS framework for conflict detection in autonomous drone navigation

The fast growth of autonomous drone activities in urban airspace poses major challenges in real-time conflict detection during uncertain and dynamic conditions. The current models of decision making usually employ simplistic representations of uncertainty and less objective weighting models, which restrict their efficacy in safety–critical settings. In order to fill this gap, this research will introduce a new multi-criteria decision-making model using Fermatean fuzzy set (FFS) combined with CRITIC (Criteria Importance through Intercriteria Correlation) and WASPAS (Weighted Aggregated Sum Product Assessment) models. FF environment can model higher order uncertainty and hesitation in a better way, and the CRITIC approach can offer objective criteria weight that takes into consideration inter-criteria conflict and variability. The WASPAS approach is used to obtain a strong and stable ranking of conflict-resolution options by aggregating both additively and multiplicatively. The suggested framework is justified in a simulation-based case study and a comparison to benchmark methods like TOPSIS and VIKOR. The findings indicate that there is a consistent behavior in the ranking and better stability under changing conditions as attested by the sensitivity analysis, which underscores the soundness of the proposed method in dealing with uncertainty and inter-criteria relationships. The research adds to the structured and objective decision-making model in detection of conflict in the face of uncertainty and serves as a foundation of real-life application in Unmanned Aerial Vehicles UAV systems.