面向自适应导航的自主水下认知系统：一种集成SLAM的认知架构
Autonomous Underwater Cognitive System for Adaptive Navigation: A SLAM-Integrated Cognitive Architecture
AI总结：
针对深海勘探中动态水下环境的导航难题，本文提出集成SLAM与Soar认知架构的AUCS系统，融合多传感器数据与认知推理模块，实现感知-认知-行动-学习闭环，提升深海自主导航的安全性与可靠性。
摘要：
深海勘探面临诸多严峻挑战，包括动态水下环境中的定向障碍、通信中断以及导航失效问题。本文提出了一种自主水下认知系统（Autonomous Underwater Cognitive System, AUCS），该系统将同步定位与地图构建（Simultaneous Localization and Mapping, SLAM）与基于Soar的认知架构相集成，可在复杂海洋条件下实现自适应导航。该系统融合来自声呐（SONAR）、激光雷达（LiDAR）、惯性测量单元（IMU）和多普勒测速仪（DVL）的多传感器数据，搭配负责感知、注意力、规划与学习的认知推理模块。与传统SLAM系统不同，AUCS引入语义理解、自适应传感器管理和基于记忆的学习机制，可区分动态与静态物体，减少错误回环检测，提升长期地图一致性。所提出的架构实现了完整的感知-认知-行动-学习闭环，使自主水下航行器能够智能感知、推理与自适应调整。本研究为下一代认知潜水系统奠定了基础，可提升深海勘探的安全性、可靠性与自主水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
