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

TL;DR

This paper introduces an autonomous underwater cognitive system that combines SLAM with cognitive reasoning to improve adaptive navigation, perception, and learning in complex deep-sea environments.

Contribution

It presents a novel integrated architecture that fuses multi-sensor data with cognitive modules, enabling semantic understanding and adaptive decision-making for underwater exploration.

Findings

Enhanced map accuracy through semantic understanding

Improved navigation robustness in dynamic environments

Demonstrated system adaptability in real-world tests

Abstract

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.