Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies

Gallala, Abir; Kumar, Atal Anil; Hichri, Bassem; Plapper, Peter

doi:10.3390/s22134950

Cited by 35 publications

(6 citation statements)

References 38 publications

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“…On the other hand, HRI is also shaped by the values, goals, and aspirations of human culture. The development and use of robots in human society are driven by the perceived needs and priorities of society, such as the desire to automate tedious tasks in the workforce, healthcare, and mental health, or create new forms of entertainment (Gallala et al, 2022;Maroto-Gómez et al, 2022;Townsend and MajidiRad, 2022;White and Hipólito, 2023). The design and implementation of human-robot interactive settings are heavily permeated by cultural values and norms.…”

Section: Preprint -Please Cite the Originalmentioning

confidence: 99%

Augmented Cognition: Life as we don't know it

Hipólito¹

2023

Preprint

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This paper proposes a framework for comprehending the integration of Artificial Intelligence (AI) as Augmented Cognition (AugCog). AugCog is viewed as an emergent bio-cultural process, reflecting AI's design, implementation, and usage. Section 1 establishes smart societies as a complex system. Section 2 defends that the development of AI is analogous to the biological process of niche construction. Section 3 defines AI as a socioculturally embodied expansion by which AI is shaped by and shapes human experience, resulting in various forms of AugCog. Section 4 highlights AugCog in mixed realities such as social media, neurotechnology, and smart environments, illustrating its emergence from multiscale and interdependent sociocultural perspectives. AugCog's perspective situates the human species in the state space of evolution, signifying our existence as a species and as embodied individuals.

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Section: Preprint -Please Cite the Originalmentioning

confidence: 99%

Augmented Cognition: Life as we don't know it

Hipólito¹

2023

Preprint

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“…They emphasized that digital twin technology is effective for predicting anomalies and enhancing worker safety. Gallala et al [ 30 ] proposed a digital twin approach for human-robot interaction (HRI) based on Industry 4.0-enabled technologies such as mixed reality, Internet of Things, collaborative robots, and artificial intelligence. They validated the proposed method using Microsoft Hololens 2 and the KUKA IIWA collaborative robot, claiming that even people without programming knowledge can achieve human–robot interaction.…”

Section: Related Researchmentioning

confidence: 99%

Digital Twin-Based Integrated Monitoring System: Korean Application Cases

Choi

Woo

Kim

et al. 2022

Sensors

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A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off problems before they occur. This paper presents a digital twin architecture and a system based on an interoperable data model. It explains how to build a digital twin for the integrated control monitoring using edge devices, data analytics, and realistic 3D visualization. The system allows continuous collaboration between field engineers for data gathering, designers for modeling 3D models, and layout engineers for layout changing by generating 3D digital twin models automatically. The system helps stakeholders focus on their respective roles to build digital twins. Examples applied to the Korean automotive parts makers are also introduced in this paper. The system can be easily used by small and medium-sized enterprises (SMEs) as well as large companies. Beyond simply watching the production site with CCTV, the production site can be intuitively managed based on the digital twin.

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“…The proposed framework can be used in real-time with industrial robots. The physical equipment consists of collaborative robots and MR head-mounted devices (MR-HMDs), while the digital components include the DT model of the robot, additional virtual objects, and a user interface (UII) that can identify and consider human gestures, sound, and motion [ 11 ]. The system operates by transmitting data through the network of cyber-physical systems (CPS) in the industrial environment to control the motion of robots wearing heavy equipment.…”

Section: Introductionmentioning

confidence: 99%

Design of Digital-Twin Human-Machine Interface Sensor with Intelligent Finger Gesture Recognition

Tien

Yeh

et al. 2023

Sensors

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In this study, the design of a Digital-twin human-machine interface sensor (DT-HMIS) is proposed. This is a digital-twin sensor (DT-Sensor) that can meet the demands of human-machine automation collaboration in Industry 5.0. The DT-HMIS allows users/patients to add, modify, delete, query, and restore their previously memorized DT finger gesture mapping model and programmable logic controller (PLC) logic program, enabling the operation or access of the programmable controller input-output (I/O) interface and achieving the extended limb collaboration capability of users/patients. The system has two main functions: the first is gesture-encoded virtual manipulation, which indirectly accesses the PLC through the DT mapping model to complete control of electronic peripherals for extension-limbs ability by executing logic control program instructions. The second is gesture-based virtual manipulation to help non-verbal individuals create special verbal sentences through gesture commands to improve their expression ability. The design method uses primitive image processing and eight-way dual-bit signal processing algorithms to capture the movement of human finger gestures and convert them into digital signals. The system service maps control instructions by observing the digital signals of the DT-HMIS and drives motion control through mechatronics integration or speech synthesis feedback to express the operation requirements of inconvenient work or complex handheld physical tools. Based on the human-machine interface sensor of DT computer vision, it can reflect the user’s command status without the need for additional wearable devices and promote interaction with the virtual world. When used for patients, the system ensures that the user’s virtual control is mapped to physical device control, providing the convenience of independent operation while reducing caregiver fatigue. This study shows that the recognition accuracy can reach 99%, demonstrating practicality and application prospects. In future applications, users/patients can interact virtually with other peripheral devices through the DT-HMIS to meet their own interaction needs and promote industry progress.

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Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies

Cited by 35 publications

References 38 publications

Augmented Cognition: Life as we don't know it

Augmented Cognition: Life as we don't know it

Digital Twin-Based Integrated Monitoring System: Korean Application Cases

Design of Digital-Twin Human-Machine Interface Sensor with Intelligent Finger Gesture Recognition

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