为月球出舱任务设计智能助手，实时提供精准操作指引。
Context Aware AI Assistant and AR Interface for Lunar Extravehicular Activity (EVA) Procedural Guidance
- 用结构化数据增强大模型，动态匹配任务上下文
- 在单故障场景下仍达8.15分（满分10分）
- 适合航天员出舱时快速获取关键步骤指引
随着人类重返月球，宇航员在出舱活动（EVA）中需快速获取程序信息，但注意力分散于导航、维修、工具操作和环境风险。问题不在于信息缺失，而在于何时何地呈现正确信息。我们提出GAIN-AI（智能导航引导助手），一种面向模拟月球EVA的上下文感知AI助手与极简抬头显示界面。系统分两层运行：第一层将大语言模型与结构化上下文结合——包括EVA流程文档、实时遥测数据及错误处理协议，均以JSON编码；第二层将输出重构为三类紧凑单元用于AR显示：目标（Goal）、任务（Task）、验证（Verification）。在111个合成EVA场景中评估，系统在正常条件下得分为10.0/10，在单故障场景下为8.15/10，多故障及边界阈值情况性能下降。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。