用BIM和大模型提升工地机器人避障能力，路径更安全。
Safe and Trustworthy Robot Pathfinding with BIM, MHA*, and NLP
- 结合BIM空间与语义信息，改进A*算法实现智能避障。
- 避障成功率提升80%，路径长度基本不变。
- 适合建筑机器人、复杂动态环境下的路径规划。
近年来，施工机器人在研发中受到广泛关注。然而，工业机器人应用面临独特挑战：动态环境、领域特定任务以及复杂的定位与建图问题。在施工现场，移动物体和复杂机械可能导致路径规划困难，存在碰撞风险。现有方法如同时定位与地图构建（SLAM）虽可行，但需高精度传感器和大量数据处理，计算开销大。本文提出利用建筑信息模型（BIM）中的空间与语义信息，构建领域专用路径规划策略。通过融合多启发式A*（MHA*）算法，结合来自BIM空间信息的势场函数（APFs），并使用大语言模型（LLMs）解析BIM文本信息以动态调整避障策略。实验表明，机器人与障碍物的接近程度提升了80%，同时保持了相近的路径长度。
原文摘要 · Abstract (English)
Construction robots have gained significant traction in recent years in research and development. However, the application of industrial robots has unique challenges. Dynamic environments, domain-specific tasks, and complex localization and mapping are significant obstacles in their development. In construction job sites, moving objects and complex machinery can make pathfinding a difficult task due to the possibility of object collisions. Existing methods such as simultaneous localization and mapping are viable solutions to this problem, however, due to the precision and data quality required by the sensors and the processing of the information, they can be very computationally expensive. We propose using spatial and semantic information in building information modeling (BIM) to develop domain-specific pathfinding strategies. In this work, we integrate a multi-heuristic A* (MHA*) algorithm using APFs from the BIM spatial information and process textual information from the BIM using large language models (LLMs) to adjust the algorithm for dynamic object avoidance. We show increased robot object proximity by 80% while maintaining similar path lengths.
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