CoBuilt 4.0: Investigating the potential of collaborative robotics for subject matter experts

• Published in: International journal of architectural computing Vol. 18; no. 4; pp. 353 - 370
• Main Authors: Reinhardt, Dagmar, Haeusler, Matthias Hank, London, Kerry, Loke, Lian, Feng, Yingbin, De Oliveira Barata, Eduardo, Firth, Charlotte, Dunn, Kate, Khean, Nariddh, Fabbri, Alessandra, Wozniak-O’Connor, Dylan, Masuda, Rin
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2020
• Subjects: Industry 4.0; collaborative robotics; machine learning; industry research; on-site robotic fabrication
• ISSN: 1478-0771; 2048-3988
• DOI: 10.1177/1478077120948742

Human-robot interactions can offer alternatives and new pathways for construction industries, industrial growth and skilled labour, particularly in a context of industry 4.0. This research investigates the potential of collaborative robots (CoBots) for the construction industry and subject matter experts; by surveying industry requirements and assessments of CoBot acceptance; by investing processes and sequences of work protocols for standard architecture robots; and by exploring motion capture and tracking systems for a collaborative framework between human and robot co-workers. The research investigates CoBots as a labour and collaborative resource for construction processes that require precision, adaptability and variability. Thus, this paper reports on a joint industry, government and academic research investigation in an Australian construction context. In section 1, we introduce background data to architecture robotics in the context of construction industries and reports on three sections. Section 2 reports on current industry applications and survey results from industry and trade feedback for the adoption of robots specifically to task complexity, perceived safety, and risk awareness. Section 3, as a result of research conducted in Section 2, introduces a pilot study for carpentry task sequences with capture of computable actions. Section 4 provides a discussion of results and preliminary findings. Section 5 concludes with an outlook on how the capture of computable actions provide the foundation to future research for capturing motion and machine learning.