Autonomous Underwater Cognitive System for Adaptive Navigation: A SLAM-Integrated Cognitive Architecture
It addresses navigational challenges for autonomous underwater vehicles in dynamic oceanic environments, representing an incremental improvement over conventional SLAM systems.
This paper tackles the problem of disorientation and navigational failures in deep-sea exploration by presenting an Autonomous Underwater Cognitive System (AUCS) that integrates SLAM with a cognitive architecture, resulting in reduced false loop closures and enhanced long-term map consistency.
Deep-sea exploration poses significant challenges, including disorientation, communication loss, and navigational failures in dynamic 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 in complex oceanic conditions. The system fuses multi-sensor data from SONAR, LiDAR, IMU, and DVL with cognitive reasoning modules for perception, attention, planning, and learning. Unlike conventional SLAM systems, AUCS incorporates semantic understanding, adaptive sensor management, and memory-based learning to differentiate between dynamic and static objects, reducing false loop closures and enhancing long-term map consistency. The proposed architecture demonstrates a complete perception-cognition-action-learning loop, allowing autonomous underwater vehicles to sense, reason, and adapt intelligently. This work lays a foundation for next-generation cognitive submersible systems, improving safety, reliability, and autonomy in deep-sea exploration.