COGNITIVE COLLABORATION-BASED DECISION-MAKING FRAMEWORK FOR MANNED/UNMANNED AERIAL VEHICLE SYSTEMS

This paper investigates a cognitive collaboration-based decision-making framework for manned/unmanned systems, aiming to address the limitations of traditional methods in situation assessment, task allocation, and path planning. Firstly, a dual layer coupled situation and threat assessment model is constructed using Dynamic Intuitionistic Fuzzy Cognitive Maps (DIFCM) and Genetic Algorithms (GA), achieving collaborative optimization of global situation inference and local threat quantification. Secondly, an adaptive task allocation mechanism is designed by integrating an improved Contract Net Protocol with UAV intelligent emotional modes, effectively balancing task execution efficiency and resource utilization. Finally, an emotion-driven improved A* algorithm is introduced, enhancing the adaptability and safety of path planning in dynamic threat environments through dynamic threat avoidance radii and cognitive load feedback mechanisms. Simulation experiments demonstrate that the proposed algorithm improves replanning response time by 23% compared to traditional path planning algorithms under sudden threats, while reducing path threat costs by 58%. The research outcomes provide new theoretical exploration and practical references for manned/unmanned collaborative tasking and decision-making in intelligent mission scenario, aiming to advance the theoretical and practical development of deep human-machine intelligence integration.