Collaborative Robotic Masonry and Early Stage Fatigue Prediction

Das, Avishek; Foged, Isak Worre; Jensen, Mads Brath; Hansson, Michael

doi:10.5151/proceedings-ecaadesigradi2019_376

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…21 This has been successfully demonstrated with related research projects using Kinect sensing for tracking brick laying. 22 There exists a range of techniques for motion capture, including relatively limited technologies such as leap motion and Kinect sensors, or full body suits with incorporated data gloves that produce highly accurate results. The resulting raw data can be exported and processed for supervised machine learning, and to complete a desired movement or task within a range of movements.…”

Section: Limitedmentioning

confidence: 99%

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

Reinhardt

Haeusler

London

et al. 2020

International Journal of Architectural Computing

View full text Add to dashboard Cite

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.

show abstract

Section: Limitedmentioning

confidence: 99%