Automated Adaptive Navigation Leveraging Natural Language Models in Aerospace Robotics System

Vision-based obstacle detection in the space environment is a fundamental task for aerospace devices to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets and employing an auxiliary network to detect changes for aerospace device. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance.