面向月球舱外活动（EVA）程序指导的上下文感知AI助手与AR界面
Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance
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中文总结 AI 辅助
针对月球EVA中宇航员注意力分散导致的信息适配难题，提出GAIN-AI系统，通过两层架构结合大语言模型与AR界面，在111个合成EVA场景中取得良好表现。
中文摘要 AI 辅助
随着人类太空探索重返月球，宇航员在舱外活动（EVA）期间需要快速获取程序信息，此时他们的注意力分散在导航、维修任务、工具操作和环境风险应对上。挑战并非信息缺失，而是在恰当的时机呈现正确的信息。我们提出GAIN-AI（智能导航引导助手，Guided Assistant for Intelligent Navigation），一款用于模拟月球EVA程序指导的上下文感知AI助手及极简平视显示界面。该系统分为两层运行：第一层以结构化上下文为基础构建大语言模型，结构化上下文包括EVA程序文档、实时遥测数据及编码为JSON的错误处理协议；第二层将输出重组为三个紧凑单元用于AR显示：目标（Goal）、任务（Task）和验证（Verification）。在111个合成EVA场景上进行评估，该系统在标称条件下得分为10.0/10，在单故障场景下得分为8.15/10，在多故障和边界阈值场景下性能有所下降。
英文摘要
As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where attention is divided across navigation, repair tasks, tool handling, and environmental risk. The challenge is not the absence of information, but surfacing the right information at the right moment. We present GAIN-AI (Guided Assistant for Intelligent Navigation), a context-aware AI assistant and minimal heads-up interface for procedural guidance in simulated lunar EVA. The system operates in two layers. The first grounds a large language model with structured context: EVA procedure documents, live telemetry data, and error-handling protocols encoded as JSON. The second restructures that output into three compact units for AR display: Goal, Task, and Verification. Evaluated on 111 synthetic EVA scenarios, the system scores 10.0/10 on nominal conditions and 8.15/10 on single-fault scenarios, with performance degrading on multi-fault and boundary-threshold cases.
发表机构
- MIT（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
