用知识图谱+大模型辅助水下无人艇操作，提升决策安全性和效率
A Cognitive Architecture for Shared Autonomy in AUV Operations
- 构建基于领域知识图谱的大模型系统，分角色支持任务全流程
- 在模拟中验证任务可行性、规划与执行，降低操作员负担
- GPT-OSS在规划与执行中表现最优，适合复杂任务决策
操作员在遥控潜水器（ROV）作业中仍至关重要，但常面临情境意识不足和高工作负荷的问题，影响安全性。本文提出一种认知架构，包含知识图谱与多个大语言模型（LLMs），在任务全阶段辅助操作员。每个大模型均基于知识图谱的领域信息，并赋予特定角色，使决策既依托领域知识，又具备大模型的推理能力。该框架可判断给定无人水下航行器（UUV）的任务可行性，完成任务规划，并在仿真中执行任务。操作员可参与规划与执行，确保计划有效性及航行安全。对比Llama3、GPT-OSS和Qwen2.5三种模型，发现GPT-OSS在可行性评估、规划与执行任务中表现最佳；而Qwen2.5最擅长从自然语言输入中识别任务类型。
原文摘要 · Abstract (English)
Operators remain essential to Remotely Operated Vehicle (ROV) operation, yet often suffer from low situational awareness and high workload, both of which negatively affect safety. This paper presents a cognitive architecture consisting of an ontology and multiple Large Language Models (LLMs) to assist the operator at all stages of the mission. Each LLM is grounded with domain-specific information from the ontology and given a simple role to create a system that can support the operator at all stages of an operation. We are aiming to prove that using the two together will allow decisions to be grounded in the relevant domain knowledge, but also benefit from the reasoning capabilities of the LLM. Our framework determines if a mission is possible for a given Unmanned Underwater Vehicle (UUV), performs mission planning, and executes a given mission in simulation. The operator can be involved in planning and execution, ensuring the resulting plan is valid and that the vehicle behaves safely during execution. We compare different LLMs, Llama3, GPT-OSS, and Qwen2.5, to determine which are best suited to the different roles within our framework. We find that GPT-OSS performs best for feasibility assessment, planning, and execution, while Qwen2.5 is best suited to identifying mission types from natural language input.
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