Digital twin-enabled robotics for smart tag deployment and sensing in confined space

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Alan Putranto , Tzu-Hsuan Lin , Ping-Ting Tsai
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引用次数: 0

Abstract

The deployment of smart sensors in confined spaces presents significant challenges due to limited visibility, environmental constraints, and communication interference. This study introduces a novel integration of digital twin technology with robotics to address these challenges, enabling precise and reliable sensor deployment in complex environments such as steel box girders. The proposed system leverages a digital twin framework for real-time simulation, calibration, and monitoring, ensuring spatial consistency between virtual and physical operations. Advanced calibration methods align the robotic arm with its 3D camera coordinates, enhancing deployment accuracy. Communication robustness is achieved by strategically prioritizing critical control and sensor signals, mitigating the impact of wireless interference in confined spaces. Additionally, the system automates the deployment of RFID-based smart sensors, incorporating 3D-printed protective casings for durability in harsh conditions. Experimental results demonstrate the system's effectiveness in overcoming spatial, visibility, and communication challenges, providing a scalable solution for structural health monitoring and other industrial applications. This study contributes a holistic and innovative robotics and digital twin integration framework in confined and complex environments.
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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