Aims:

1. Align As-Built and As-Planned Models: The primary objective is to minimize discrepancies between as-built and as-planned models, improving efficiency and accuracy in construction, with a focus on gypsum board cutting.

2. Automate Processes: An integral part of the project is to streamline the construction process by integrating advanced technologies such as robotics, BIM, and ifcOWL ontology, leading to automation in digital fabrication and, initially, in gypsum board cutting.

3. Enable Interoperability with openBIM: The project aims to emphasize and harness the interoperability of openBIM standards, demonstrating how it can contribute to more efficient and streamlined digital fabrication processes, while maintaining flexibility and adaptability to meet varied construction demands.

Scope:

The current scope of this project is focused on automating the gypsum board cutting process through a fusion of robotics, BIM technology, and ifcOWL ontology. However, the inherent interoperability of openBIM standards and the flexibility provided by the use of ontologies, means this approach can be extended to other construction processes as per demand, without significant changes. This highlights the adaptability and potential future application of this technology in the broader construction industry.

Upon commencing this project, our ambitious objectives sought to innovate the construction process through digital automation and interoperability. We've made significant strides, successfully integrating intricate systems. Though our objectives have been met and our prototype functions effectively, we recognize there's still room for enhancement and growth. This realization reflects our commitment to continuous improvement and innovation in the construction industry.

       Objectives of Project:

1. Efficient Integration: The primary objective is to effectively integrate ROS with ifcOWL ontology and IFC model data, overcoming the complexities of these systems.

2. Establish Reliable Data Extraction and Processing: The project aims to develop a robust workflow for extracting and panelizing wall geometry data from IFC models, ensuring accuracy and consistency.

3. Develop Simulation and Visualization Methods: The project seeks to create methods for simulating and visualizing robotic movements and fabrication processes, integrating with tools like RVIZ for real-time feedback and adjustments.

4. Ensure Synchronization and Updates: A key objective is to create a system that synchronizes the IFC model data with the fabrication process, updating the panel geometry and fabrication status on the server accurately.

5. Ensure Scalability and Flexibility: The project aims to create a solution adaptable to various fabrication tasks beyond gypsum board cutting, indicating its scalability and flexibility.

6. Plan for Future Improvements: Lastly, the project aims to identify potential areas for further research, development, and collaborations, considering the application of the solution in real-world scenarios.

Numerous past attempts to automate digital fabrication in construction by integrating BIM and robotic systems have encountered various limitations:

1. Dependency on Ontology & Semantic Web: Our methodology heavily relies on ontology, specifically, the IFC model being converted to ifcOWL. While there is a Github repository available for this conversion, this dependence is a limitation.

2. Restricted Application: Initially, our method was specifically applied for gypsum board cutting. Although it possesses scalability, its application beyond this remains to be explored. However, the potential for the project to be extended exists, and gypsum board cutting merely represents the initial stage.

3. Limited Ontology and SPARQL Expertise: The project utilizes the SPARQL query method and ontology, a combination that's not commonly used in the industry, resulting in a shortage of experts. While this is a current limitation, the rising popularity of ontology due to its data integration capability suggests that this approach will be increasingly suitable soon.

To successfully initiate and implement this use case, the following competencies are prerequisites:

1. Proficiency in Programming: The necessary scripts and related documentation are accessible via our Github repository. However, the successful configuration and deployment of these libraries necessitate a substantial level of coding proficiency.

2. Familiarity with Ontology: In the event that expansion of this method is desired, an in-depth understanding of ontology is indispensable. This knowledge domain is critical for facilitating the method's scalability and adaptability.

3. Expertise in Machine Operations and Robotic Programming: The installation and calibration of the cutting machine, and its subsequent integration into our script, require a high level of skill in both machinery operation and robot coding. These technical competencies are essential to the successful execution of the project.