# Digital Twin for Human-Robot Collaboration in Manufacturing: Review and Outlook
## 1 Introduction  
This new era of industrial digitalization, commonly known as Industry 4.0, focuses on interconnected smart machines [1; 2]. Some of the key goals of Industry 4.0 technologies include the development of an open, smart, manufacturing platform, the transformation of machines into a self-aware, self-learning system by allowing each machine to interact with sensors and other machines, and the use of data collected from sensors to aid production and organizational decision making [3]. The application of cyber-physical systems (CPS) along with robotics and digital software technologies, such as computer-aided design (CAD), computer-aided manufacturing (CAM), simulation, system modelling, product lifecycle management (PLM) and enterprise resource planning (ERP) systems are widespread across multiple manufacturing domains. The integration of these technologies forms the foundational architecture of Industry 4.0 [4; 5; 6]. Industry 4.0, therefore, enables companies to adapt quicker to the needs of global markets and equires them to face global challenges [7]. The technologies developed as part of Industry 4.0 enable the generation and communication of real-time information about machines, robots and operators. They also enable the development of robots that can work collaboratively with operators. Robots are used widely in manufacturing to carry out repetitive and strenuous tasks. The size of the global market for industrial robots has been increasing, with almost 2.1 million new robots being installed between 2018 and 2021 [8]. Collaborative robots that can work alongside humans are also used in manufacturing; however, these are designed or programmed to complete a specified task [9]. Collaborative robots use varioussafety sensors to prevent any hazardous physical interaction with a human. Unlike their industrial counterparts, such robots are much safer and do not require special work cells in which to operate safely. It is expected that in the near future, intelligent collaborative robots and humans will carry out manufacturing tasks by combining their respective skill sets [10]. One of the main concerns with using such intelligent collaborative robots is the safety of the operator while partnering with the robot to carry out the tasks [11]. Safety is an important issue, especially for autonomous robotic tasks in a broad range of applications [12]. One approach to address this problem is to develop a simulation model to replicate and test the actual scenario before it is deployed on the shop floor. This process has been further enabled by the development of IoT and CPS systems that have resulted in high fidelity virtual simulation models capable of acquiring the properties of the physical system. Simulation models that precisely demonstrate a physical system in real-time are known as digital twin (DT) [13; 1]. The DT can operate in synchronization with the physical system by utilizing the data acquired and exchanged via the IoT network [14]. DTs, which have evolved using the technological platform of Industry 4.0 [15; 16], provide robust models to evaluate, in a virtual manner, the performance of the human and the robot before deploying in the real environment. In recent years, interest in DT has grown significantly [17]. For instance, the value of the DT market was $2.26 billion in 2017 and is expected to reach $26.07 billion by 2025 [18]. Data gathered from Google trends (Figure 1) illustrates the pattern of individual search terms \"DT\" and \"CPS\", with a year-on-year increase evident for DT. It is argued that the concept of DT has gained popularity due to its potential application across a wide range of sectors, including automobile, manufacturing and aerospace [19]. Given the range of studies and literature published in the area of DT recently, the focus of this paper is to review the current trends in DT and to discuss the development of DT for human-robot collaboration (DT-HRC) scenarios.