A Human–Robot Team Knowledge-Enhanced Large Language Model for Fault Analysis in Lunar Surface Exploration - Wang, Hao; Xue, Shuqi; Zhang, Hongbo; Wang, Chunhui; Fu, Yan (Aerospace, 2025)
Human–robot collaboration for lunar surface exploration requires high safety standards and tedious operational procedures. This process generates extensive task-related data, including various types of faults and influencing factors. However, these data are characteristic of multi-dimensional, time series, and intertwined. Also, prolonged tasks and multi-factor data coupling pose significant challenges for astronauts in achieving safe and efficient fault localization and resolution. In this paper, we propose a method to enhance the base large language models (LLMs) by embedding knowledge graphs (KGs) of lunar surface exploration, thereby assisting astronauts in reasoning about faults during the exploration process. A multi-round dialog dataset is constructed through the knowledge subgraph embedded in the request analysis process. The LLM is fine-tuned using the p-tuning method to develop a specialized LLM suitable for lunar surface exploration. With reference to the situational awareness (SA) theory, multi-level prompts are designed to facilitate multi-round dialogues and aid decision-making. A case study shows that our proposed model exhibits greater expertise and reliability in responding to lunar surface exploration tasks than classical commercial models, such as ChatGPT and GPT-4. The results indicate that our method provides a reliable and efficient aid for astronauts in fault analysis during lunar surface exploration.