将认知架构与SLAM结合，让潜水器在深海自主导航更智能可靠。
Autonomous Underwater Cognitive System for Adaptive Navigation: A SLAM-Integrated Cognitive Architecture
- 用认知系统融合多传感器数据，实现感知-推理-行动闭环。
- 能区分动静物体，减少误闭环，提升长期地图一致性。
- 适合深海探索、自主潜航器研发人员参考。
深海探测面临方向迷失、通信中断和导航失败等挑战。本文提出一种自主水下认知系统（AUCS），将同步定位与建图（SLAM）与基于Soar的认知架构结合，实现复杂海洋环境下的自适应导航。系统融合声呐、激光雷达、惯性测量单元（IMU）和多普勒速度计（DVL）的多源数据，通过感知、注意力、规划与学习模块进行认知推理。相比传统SLAM系统，AUCS引入语义理解、自适应传感器管理及记忆学习机制，可有效区分动态与静态物体，降低误闭环率，增强长期地图一致性。该架构实现了完整的感知-认知-行动-学习循环，使自主水下航行器具备智能感知与适应能力。本研究为下一代认知型潜水器奠定了基础，显著提升深海探索的安全性、可靠性与自主性。
原文摘要 · Abstract (English)
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.
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