Search for: All records

Autonomous navigation in construction environments is particularly challenging due to dynamic obstacles and uncertain surroundings. While recent advances in Building Information Modeling (BIM)-based planning have leveraged spatial and semantic information to improve navigation, most prior work assumes precise localization of the BIM model to enable global path planning. In contrast, this paper introduces an online replanning framework that registers obstacles on discovery within BIM and replans according to the updated semantic map. Our method integrates object-aware path planning by utilizing large language models (LLMs) to extract semantic danger sentiments from BIM-annotated objects and their spatial information about the mission environment. Additionally, we demonstrate practical feasibility by integrating a path tracking control, ensuring generated paths are not only safer but also realistically executable by mobile robots. Experimental results demonstrate an improved obstacle avoidance by 2.8× compared to traditional A* algorithms in dynamically updated environments.