建築資訊模型(BIM)與機器人規劃器之間的資料整合對於建築機器人預製化的成功執行至關重要。然而，過去在兩者之間仍然面臨著資料整合不足所引發的一系列挑戰，包括資料遺失、錯誤檢測的困難以及複雜的資料管理。因此，本研究旨在提出一種基於 BIM 的機器人建築預製資料整合方法，以應對這些挑戰。
本研究提出的方法包括三個模組:資料儲存解決方案、BIM 資料處理模組和機器人資料載入器模組。資料儲存解決方案結合了預定義的資料結構和專用資料庫，用於儲存與機器人規劃過程相關的資訊。BIM 資料處理模組從 BIM 模型中提取必要的資料，並使用 3D 座標轉換器，以遵循所提出的資料結構。機器人資料載入器模組將處理後的資料平滑地導入機器人規劃器，以實現規劃過程中的 3D 模型生成。
透過對木質牆架的案例研究，驗證了所提出方法的可行性，同時展示了機器人規劃器中組件成功重建的成果。研究結果顯示了資料整合方法的可行性，並為機器人 建築預製提供了標準化的資料結構和指導原則。本方法簡化了資料轉換的過程，同時增強了 BIM 設計與機器人規劃器之間的資料交流，並賦予製造商更靈活運用機器人製造技術的能力。

Achieving smooth data integration between Building Information Modeling (BIM) and robot planners is crucial for successfully implementing robotic building prefabrication. However, the current lack of integration poses challenges such as data loss, error detection difficulties, and complex data management. This research aims to address these challenges by proposing a data integration method for BIM-based robotic building prefabrication. The proposed method has three modules: a data storage solution, BIM data processing, and a robot data loader. The data storage solution incorporates a predefined data schema and a dedicated database to store relevant information for the robotic planning process. The BIM data processing module extracts the necessary data from BIM models, aligns the coordinate system using a 3D transformation converter, and follows the proposed data schema. The robot data loader module smoothly imports the processed data into the robot planner, enabling the generation of 3D models for the planning process. The method is validated through a case study involving timber wall frames, showcasing the successful regeneration of components in the robot planner. This research demonstrates the effectiveness of the data integration method, providing a standardized approach for data schema and criteria in robotic building prefabrication. The method simplifies data conversion, enhances data exchange between BIM design and robotic process planning, and enables manufacturers to leverage robotic fabrication with greater adaptability.

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