![]() Furthermore, the first prime example of DT implementation was observed in 1970 in space robotics. ![]() It was then dubbed “living models” by NASA. Ironically, the first digital twin methods which were used in space robotics date back to the 1960s, when the phrase had not even been invented. As a result, this research would like to propose an adequate definition of DT Incorporated Robotics as- “A data or model based digital replica of a robotic system that allows for multi-physics, high-fidelity, multi-scale experimentable simulations through simultaneous and bidirectional state updates over the active life-cycle of the system.” Throughout this paper, the DT Incorporated robotics concept followed the proposed definition. Albeit there was a plethora of concepts and definitions for “Digital Twin”, no specific definitions were addressed for DT in Robotics or DT Incorporated Robotics. Rather, they referred to such a digital replica as a “digital shadow.” The articles state that for a digital clone to be termed a digital twin, data transfer between them must be bi-directional. Several research, however, argued that a one-way connection from a physical system to a digital simulation model could not be termed a digital twin system. Glaessgen and Stargel (2012) from NASA provided arguably the most detailed and well-known definition in of digital twin as a multi-physics, multi-scale, probabilistic simulation of a complex system that uses the best available physical models, sensor updates, and so on to replicate the life of its corresponding twin. The communication infrastructure between the physical and digital components, which is generally unilateral or not taken into account, differs between concepts like the Digital Shadow (DS) and Digital Model (DM). ![]() In other studies “Digital Twin” (DT) has been used erratically to describe various connections between the physical and digital components in the growing volume of literature. On a fundamental level, a Digital Twin of a system or component is the digital replica of the latter that mirrors and or twins the physical component through out its active life-cycle. One of the most notable among these fusions is the incorporation of Digital Twins (DT) into various fields of robotics, or more precisely “DT incorporated robotics” as has been addressed in this paper. Apart from the industrial aspect, the research sectors are also being greatly benefited through the use of such technologies. Simultaneously, it has also led to the emergence of some new concepts such as Operator 4.0 and Space Factory 4.0, , where these technological amalgams are seemingly thriving. In this new era, where industry, automation and man-machine interaction go hand in hand, a slew of new synergistic fusions of preexisting and novel technologies have been observed to have experienced a staggering rise. Human civilisation has undergone a shift in technological paradigm with the advent of Industry 4.0. AI artificial intelligence AMR autonomous mobile robot ANN artificial neural network AR augmented reality ARX auto-regressive exogenous ATV automated transfer vehicle CNN convolutional neural network CSI confident safety integration DNN deep neural network DTIR digital twin incorporated robotics DRL deep reinforcement learning DT digital twin FFNN feed-forward neural network FFT fast fourier transform GGS grasps-generation-and-selection HCMI human-computer-machine interaction HMI human-machine interface HRC human robot collaboration IIoT industrial internet of things IoT internet of things ISS international space station LbD learning by demonstration MBSE model-based systems engineering MMI multi-modal interaction MR mixed reality PCA principal component analysis R-CNN region-based convolutional neural network RDT robotic digital twin ROS robot operating system RUL remaining useful life RWCS robot work-cell simulation SVM support vector machines TENG triboelectric nanogenerator THT through hole technology VTB virtual test bed VR virtual reality XR extended reality
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