AI 中文总结
研究针对室内机器人复杂任务中建筑信息利用不足问题，提出基于BIM和智能体的模拟平台，将室内环境映射为图，用图论算法规划导航路径，经模拟验证其有效性，为BIM智能机器人系统奠定基础。
AI 中文摘要
室内机器人越来越多地用于设施管理任务，如清洁和检查，现有导航方法对环境理解有限。建筑信息模型（BIM）包含丰富信息却在机器人应用中未充分利用。本研究提出一个基于BIM和智能体的模拟平台用于知识驱动的室内机器人导航与操作规划。室内环境离散化为网格单元并映射到图节点，根据与建筑元素的空间关系分类，为连接相邻节点的边分配遍历成本，用图论算法计算导航路径。模拟结果表明该图表示能实现高效无碰撞导航，通过网格细化减轻了粗离散化的关键限制，提高了空间精度和路径可行性。该平台为设施管理中基于BIM的机器人系统提供了基础。
英文摘要
Indoor robots are increasingly employed for facility management tasks such as cleaning and inspection. These applications primarily rely on navigation and can be effectively supported by predefined routes or perception-driven Simultaneous Localization and Mapping (SLAM) techniques. However, more complex tasks, such as locating and repairing leaking pipes, require not only navigation but also access to building information, including the location, geometry, material, and operational attributes of components. Existing navigation approaches provide only limited environmental understanding and cannot readily supply such information. In contrast, Building Information Modeling (BIM) contains rich geometric, semantic, and operational information that remains largely underutilized in robotic applications. This study proposes a BIM-enabled, agent-based simulation platform for knowledge-driven indoor robot navigation and operation planning. Within the framework, indoor environments are discretized into grid cells that are mapped to graph nodes and classified as target, obstacle, or regular nodes according to their spatial relationships with building elements. Traversal costs are assigned to edges connecting neighboring nodes, enabling graph-theoretic algorithms to compute efficient and collision-free navigation paths while avoiding obstacles. Simulation results demonstrate that the proposed graph representation enables efficient and collision-free navigation. A key limitation associated with coarse discretization, namely overlap between target-occupied and obstacle-occupied cells, is identified and mitigated through grid refinement, improving spatial accuracy and path feasibility. The proposed platform supports virtual evaluation of robotic operations prior to deployment and provides a foundation for BIM-informed robotic systems in facility management.
