Problem
Research questions and friction points this paper is trying to address.
Remotely Operated Vehicle (ROV)
situational awareness
workload
safety
A Cognitive Architecture for Shared Autonomy in AUV Operations
🤖 AI Summary
为解决ROV操作中操作员的情境感知低和工作负荷高问题，本文提出一种结合本体和多个大型语言模型的认知架构，以支持任务的各个阶段。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cognitive Architecture
Large Language Models (LLMs)
Shared Autonomy
Ontology
Mission Planning
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