Digital Twin Framework for Real-time Monitoring of Robot-Arm in Construction Site

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Abstract
In recent times, there has been notable progress in the advancement of robotic arm utilization within construction operations, either as replacements for or collaborators with human labour, enhancing the efficiency of task execution. However, the integration of robot arms at construction sites introduces a pronounced susceptibility to potential accidents, necessitating vigilant and ongoing supervision. Concurrently, significant strides have been witnessed in enhancing the quality and safety oversight of robotic arms within the industrial domain. In light of these developments, this scholarly article presents an innovative framework for the real-time onsite monitoring of robotic arms engaged in indoor construction activities, underpinned by the foundational principles of the Digital Twin paradigm, referred to as iTWIN. The articulated framework comprises two interrelated modules: (1) iTWIN-visualize, a dynamic system engendering a contemporaneous digital emulation of the target activity to facilitate comprehensive quality assessment and progressive monitoring, and (2) iTWIN-monitor, a component dedicated to the identification and demarcation of hazardous zones, concomitantly tracking the movement patterns of onsite personnel to ensure their safety. The conceptual model is substantiated and validated through an illustrative case study, wherein the efficacy and viability of the proposed system are rigorously examined. The empirical findings gleaned from the study incontrovertibly establish the successful realization of a digital twin representation of the designated construction activity concomitant with the robust surveillance of worker well-being. The discernible outcomes augur a prospective trajectory wherein the pervasive adoption of the iTWIN framework is anticipated to engender heightened precision and safety in the integration of robotic arms within the construction milieu, thereby enhancing operational accuracy and minimizing potential risks.

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