Autonomous Underwater Cognitive System for Adaptive Navigation: A SLAM-Integrated Cognitive Architecture

Abstract--Deep-sea exploration faces critical challenges including disorientation, communication loss, and navigational failures in hostile underwater environments. This paper presents an Autonomous Underwater Cognitive System (AUCS) that integrates Simultaneous Localization and Mapping (SLAM) with a Soar-based cognitive architecture to enable adaptive navigation under dynamic oceanic conditions. The system combines multi-sensor fusion (SONAR, LiDAR, IMU, DVL) with cognitive reasoning capabilities including perception, attention, planning, and learning. Unlike conventional reactive SLAM systems, AUCS incorporates semantic understanding, adaptive sensor management, and memory-based learning to distinguish between dynamic and static objects, thus reducing false loop closures and improving long-term map consistency. This work addresses critical safety limitations observed in previous deep-sea missions and establishes a foundation for next-generation cognitive submersible systems.