applied
sciences
Review
Digital Twin for Human–Robot Collaboration in
Manufacturing: Review and Outlook
Aswin K. Ramasubramanian, Robins Mathew, Matthew Kelly, Vincent Hargaden
and Nikolaos Papakostas *
Laboratory for Advanced Manufacturing Simulation & Robotics (LAMS), School of Mechanical & Materials
Engineering, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland;
aswin.ramasubramanian@ucdconnect.ie (A.K.R.); robins.mathew@ucd.ie (R.M.);
matthew.kelly@ucdconnect.ie (M.K.); vincent.hargaden@ucd.ie (V.H.)
* Correspondence: nikolaos.papakostas@ucd.ie; Tel.: +353-(0)-17161741
Abstract: Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical
systems. These technologies can also support the creation of virtual replicas which exhibit real-time
characteristics of a physical system. These virtual replicas are commonly referred to as digital twins.
With the increased adoption of digitized products, processes and services across manufacturing
sectors, digital twins will play an important role throughout the entire product lifecycle. At the same
time, collaborative robots have begun to make their way onto the shop floor to aid operators in
completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide
insights into approaches used to create digital twins of human–robot collaboration and the challenges
in developing these digital twins. A review of different approaches for the creation of digital twins is
presented, and the function and importance of digital twins in human–robot collaboration scenarios
are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the
complexities of modelling the digital twin of human–robot collaboration and the exactness of the
digital twin with respect to the physical system.
Citation: Ramasubramanian, A.K.;
Mathew, R.; Kelly, M.; Hargaden, V.;
Keywords: collaborative robot; digital twin; digital manufacturing; human–robot collaboration
Papakostas, N. Digital Twin for
Human–Robot Collaboration in
Manufacturing: Review and Outlook.
Appl. Sci. 2022, 12, 4811. https://
doi.org/10.3390/app12104811
Academic Editor: Junfeng Wang
Received: 19 February 2022
Accepted: 30 April 2022
Published: 10 May 2022
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Attribution (CC BY) license (https://
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4.0/).
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 computeraided 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 [4–6]. Industry 4.0, therefore, enables
companies to adapt quicker to the needs of global markets and equips 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 various
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safety 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) [1,13]. 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].
Figure 1. Search interest and technology maturity curve of CPS and DT, since 2016. The graph
illustrates the search interest for digital twin (DT) and cyber–physical systems (CPS) in Google search
engine from Jane 2016–September 2021. The trend suggests that the focus towards DT is growing.
Data source: Google Trends.
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. Furthermore, this paper provides a
review of the various methods that are used in DT-HRC for manufacturing processes. The
remainder of this paper is structured as follows: Section 2 provides an overview of DT and
HRC, outlining definitions and future potential. In Section 3, the two approaches used to
model DT, namely the simulation and layer based approach, are described, with a particular
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focus on the modelling of human-robot collaboration. Section 4 outlines the challenges
facing DT, particularly related to the field of DT-HRC. Finally, the paper concludes with
prospects and recommendations for future research.
2. Potential of Digital Twin and Human–Robot Collaboration
Through Industry 4.0 developments, the building of highly accurate virtual replicas
of physical entities in manufacturing is now possible, which then enables control and
visualization of factory operations [15]. The resulting DTs can be coupled with HRC
operation to evaluate and ensure safe cooperation between a human operator and the
robot [20]. A DT model consists of a physical environment and a virtual environment. The
changes occurring in the physical environment are continuously monitored, tracked and
updated in the virtual environment [21,22]. Figure 2 illustrates an abstract representation of
a digital twin model of a motor. In the scenario shown, the physical system’s (motor) state
is captured using sensors and communicated to the virtual model (virtual replica of the
motor). The virtual model recreates the exact state and conditions, such as field currents,
motor temperature, state-rpm, on/off and computes the motor’s efficiency. The value of
the process parameters to run the motor more efficiently can also be calculated using this
virtual model. Furthermore, the optimal process parameters obtained from the virtual
model can be inputted to the physical model to operate the motor more efficiently [23].
Figure 2. An abstract representation of the digital twin (DT) model of a motor. A DT model recreates
real-world entities in a virtual environment with the exact characteristics of the real system. With the
information from the sensors, the virtual model will reproduce the exact state and condition of the
real-world system.
DT models operate based on multiple underlying components: CAD and CAM data,
inventory data, PLM, simulations and real-time sensor information to digitally integrate processes from design to manufacturing, with a seamless transfer of data between systems [24].
DT provides flexibility in simulating and analysing various scenarios without modifying
the physical setup or building physical prototypes. It can also support identification of
unforeseen issues in the process design. DT can facilitate the early detection of random
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behaviours and help overcome them at a very early stage [25]. Various companies, such as
Dassault, Siemens, ANSYS, Boeing and GE incorporate DT within their business lines [26].
Some of the challenges and requirements of implementing alternative approaches to DT
technologies are identified and addressed in [26].
The term DT was presented for the first time at a conference on product lifecycle
management (PLM) in 2003 by Michael Grieves. At this stage, DT was relatively immature
due to the lack of knowledge in the field [27]. An early DT model was developed by
NASA and the United States Air Force for the systems used for aerospace equipment
maintenance [13]. The model was subjected to simulated flight conditions and these
simulations were carried out using data from an actual space vehicle [28]. In the early stages
of its development, the most widely used application of DT was related to identifying faults,
performing predictive maintenance, machine health monitoring, performance and quality
analysis [29]. Today, as the manufacturing industry is seeking to develop highly automated
processes, DT will play an important role, particularly when it comes to collaborative
robots. Collaborative robots are robots that are specifically designed to work alongside
humans without the need for any safety or protective barriers that are otherwise present
in an industrial robotic system/robotic cell [30]. Collaborative robots are also capable of
physically interacting with a human in a safe manner. These capabilities are achieved by
using sensors and safety features embodied within the robot to sense the environment,
especially the presence of humans and other obstructions, and to act accordingly. Using
built-in sensors, data related to the state of the robot joints, compliance control, force, and
impedance control is readily accessible and can be utilized for developing a DT.
The concept of collaborative robots was introduced in the automotive industry during
the late 1990’s to increase the safety, quality, and performance and improve the ergonomics
of assembly processes [31]. Since then, the capabilities of collaborative robots have increased
over the years, initially from using a single-arm collaborative robot to the use now of dualarm collaborative robots to perform manufacturing tasks [32,33]. The main advantage of
using collaborative robots within the manufacturing sector is their ability to work together
with the operator without the necessity of being located in a secure enclosure [34,35]. The
terms collaboration and cooperation represent two different aspects of HRC. Collaboration
means working together to achieve a common goal, whereas cooperation means when the
operator and robot work together to achieve a shared goal [36]. Furthermore, depending on
the scenario, the relationship between humans and robots can be classified as coexistence,
interaction, cooperation and collaboration [37].
Conventional human–robot interaction approaches often lack feedback about the environment around the robot, which can cause stress and fatigue to the operator [38]. On the
other hand, the symbiotic HRC approach involves sensors that offer more information about
the environment, leading to better planning of tasks, ease of programming the robot, natural form of interaction and decision support [35]. As a result, much of the research related to
HRC aims to achieve improved communication and task planning [39], interaction [40–42],
trust between the robot and the operator [10,34], and safety [34,41]. Recent developments in
the area of industry 4.0 [43], artificial intelligence (AI) [40], DT [22], non-invasive operator
tracking utilising sensors such as somatosensory systems [44], wearable devices and visionbased tracking [35,45], and augmented/virtual reality (AR/VR) [42,43] provide additional
mechanisms to increase the safety and efficiency of operations and understanding the intentions of the human operator. They also allow more robust methods for humans and robots
to work together in a safe, collaborative environment. Operators tend to hone their skills
over a period of time, making them more adaptable to various circumstances and enabling
them to make decisions in a continuous and dynamic environment. Furthermore, research
in the area of bionics and robotic prosthetics can aid humans to effectively participate in
HRC in manufacturing environments [46,47]. However, robots require programming or
teaching to handle unexpected situations [35]. Hence, the safety of the operator working
closely with the collaborative robots should be carefully analyzed [43]. There are various
challenges involved in the modelling of HRC to establish safety and trust between the
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human and the robot. It is necessary to continuously monitor the activities of a HRC system
to make the robots adapt to the operator [10,48]. Therefore, DT in HRC could assist in the
development of more reliable and trustworthy collaborative scenarios by planning and
testing multiple scenarios and configurations of the HRC system in a virtual manner using
a DT model.
3. Review of DT in HRC
3.1. Summary of Publications
To evaluate the academic research carried out to date related to DT in robotics, Scopus
and Google Scholar databases were used to obtain data on the number of articles published
and related research topics since 2016.
Table 1 outlines the number of publications listed in Scopus, using the keywords
“Digital Twin” & ”Robotics” and “Digital Twin” & “Human-Robot Collaboration”. This
search was carried out in September 2021. The results focus on the papers that were
published in the engineering domain. Table 1 provides an overview of the categories
of research articles available. More than 50% of the documents available in Scopus are
conference publications compared to journal publications. The keywords were also used
to find the number of publications listed in Google Scholar. These results are shown in
Figure 3. Based on the results, it is clear that the number of publications in the area of DT
and HRC is increasing.
Figure 3. The number of papers found in Google Scholar from the period of 2016—2021. The
keyword used for the search is “Digital Twin” and “Robotics” for the data represented in black and
the keyword used for the data in grey is “Digital Twin” and “Human Robot Collaboration.” The
trend line indicates that interest in these areas has increased significantly since 2016.
3.2. Definitions
Though the concept of DT dates back to the early 1990s, the definition of DT was first
published in the NASA Modelling, Simulation, Information Technology, and Processing
Road Map in 2010 [13,14,49,50]. According to this definition, DT is “An integrated multiphysics, multi-scale, probabilistic simulation of a vehicle or system that uses the best
available physical models, sensor updates, fleet history, etc., to mirror the life of its flying
twin” [51]. The definitions widely used in the literature from 2010 to 2016 are summarized
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in [13] and from 2014 to 2020 in [52]. There is a wide array of definitions for the term
DT in the literature [1,13,17,53,54]. However, the concept of DT lacks clarity in a number
of areas, such as the definition, interpretation and implementation. For instance, when
DT is compared with system emulation, which is the concept of using an application to
imitate a system behaviour, it is defined as “A Digital Twin is the digital representation of a
product or system under development representing a functionally correct, predictable and
reproducible representation of the product or system at the appropriate level of fidelity to
perform verification, performance analysis and system validation tasks” [55]. In summary,
a common understanding of the term DT is that it is a high fidelity multi-physics digital
model capable of describing the micro and macro features and mirroring the state of and
the behaviour of the physical system. DT also has the capability of performing simulation
in the virtual model and mirroring the same to the physical model in real-time. Another
feature of DT is the two-way real-time communication that exists with the physical system.
In general, the definition of a DT tends to be based on an understanding of the concepts,
the context and the system for which a particular DT is developed.
Table 1. Scopus indexed publications related to keywords “Digital Twin” & “Robotics”, and “Digital
Twin”, & “Human-Robot Collaboration” in the domain of engineering.
Scopus Keywords
“Digital Twin” and
“Robotics”
“Digital Twin” and
“Human-Robot
Collaboration”
Total Number
of Papers
275
30
Publication Year and
Number of Papers
2016—04
2017—07
2018—19
2019—60
2020—95
2021—90
2017—01
2018—04
2019—08
2020—05
2021—12
Source Type
Conference
Proceeding—155
Journal—92
Book Series—26
Book—02
Conference
Proceeding—13
Journal—12
Book Series—04
Book—01
DT can be defined in many ways due to the difference in understanding of DT as a
model, simulation technology, or its association with IoT or CPS systems. This difference
is based on the level of integration and mode of exchange of data [13,53]. For instance,
digital representations such as a digital model, a digital shadow and a digital twin are often
confused with one another [1]. Figure 4 illustrates the distinction between these three in
the mode of exchange of data between the physical and virtual models. There are two
modes of data exchange, namely manual and automated. A change in the physical model
that automatically updates the virtual model and vice versa is defined as an automated
data exchange, whereas when the physical model does not update changes observed to
the virtual model and vice versa, it is described as manual data exchange [1]. Although
the underlying functionality of CPS and DT is to integrate the physical and the virtual
world, the critical difference between them is the relative focus of each of the technologies.
The core of the DT relies on the virtual model and the data to model the characteristic
behaviour of the product, whereas the core of CPS involves the integration of the sensors
and actuators [27].
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Figure 4. Representation of transfer of data between the physical model and the virtual model.
In a digital system, the type of exchange of data describes the form of the digital
system. The mode of interaction and exchange of data between the physical model and
the virtual model can be manual or automated. If the transfer of data between both the
models is manual, it is known as a digital model. Likewise, if the exchange of data between
the models is automated, it is known as a digital twin. Finally, if the data transfer from
the physical model to the virtual model is automated and not vice versa, it is called a
digital shadow [1].
3.3. Approaches in DT
A DT which focuses on a model-based simulation that integrates the manufacturing
processes with the entire product lifecycle is the next wave in modelling, simulation and
optimization in the manufacturing domain [28]. However, some researchers conceptualize
DT as a system that incorporates the physical characteristics to the digital/virtual counterpart via sensors that capture and exchange data to the virtual world, thereby replicating
the characteristics of the physical model in a virtual environment [14,21]. A structured
approach to review DT concepts in the manufacturing domain is shown in [17]. This work
systematically reviews the current status of DT in the manufacturing domain by providing
an overview of the concepts, classification of literature, product lifecycle for applications
and direction for further research. The DT of a specific system or an object can be classified
into two categories: (i) DT Prototype—consisting of all necessary information to develop
a physical model which can be connected to a virtual model such as a CAD model, or
(ii) DT Instances—a DT of a model which is connected to the physical product throughout
the entire lifecycle of the product, such as geometrical information or operational states
from sensors [52,56]. Therefore, the concept of DT can generally be classified into two
categories; simulation-based approach (SBA) and layer based approach (LBA). This classification of DT into two approaches is based on understanding from the different streams of
literature that address various stages of the product development process involving DT.
However, there are different aspects within these two approaches to create a DT. These
two approaches, their variants, comparisons and underlying logic are explained in the
following sub-sections.
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3.3.1. Simulation-Based Approach
The simulation-based approach (SBA) combines the digital models and the data
generated at each stage of the product development lifecycle to build the DT [23,28,49,57].
For this, the real-world object’s physical, geometrical and behavioural properties are
required. Figure 5 gives an overview of the various steps in the development of DT via
SBA. The SBA primarily focuses on the collection of data via multiple simulation models
at various phases of the product lifecycle, such as the design, engineering, operation and
service phases. SBA uses a linear approach where the information gathered at each stage
is used to update the digital twin model and maintain a complete record of the product
lifecycle as it evolves [58]. In SBA, the DT evolves with the real product by embedding
various characteristics and properties of the product [23]. Additionally, the modules used
across all four phases of the DT model in SBA need to recognise the data formats used
by all other modules. Therefore, the digital thread will play a crucial role in the future
of DT under SBA architecture [23,59,60]. Digital thread and data-driven communication
architectures can provide seamless integration and transfer of data across the complete
product lifecycle. Digital thread supports the process by having a unified data format with
all the information required for the exchange of data between the physical and virtual
models of the DT [60].
Figure 5. Overview of simulation-based DT. The data collected at each stage of the product
Figure 5. Overview of simulation-based DT. The data collected at each stage of the product development are used to create a digital instance of the physical object. The digital object is developed in
parallel with the physical object.
During the development of the DT using SBA, the DT model is commissioned virtually before the operation phase to evaluate the performance and to identify and solve any
shortcomings of the model [61–63]. Virtual commissioning can be carried out either by
using a hardware-in-the-loop simulation or by using a software-in-the-loop simulation [64].
In a hardware-in-the-loop simulation, the virtual plant is controlled using actual control
hardware. In a software-in-the-loop simulation, the virtual plant is controlled by using a
virtual controller. A framework that can automate the process of decision making for a
simulation-based DT of a CPS is illustrated in [65]. The software-in-the-loop virtual commissioning enables the designer to validate the hardware system before it is manufactured [66].
This is particularly helpful in the case of HRC, as the system can be tested for safety before
being physically implemented on the shop floor.
The virtual commissioning of a collaborative robot for a bin-picking application by
using software-in-the-loop simulation is explained in [67]. Experimental digital twin, as
stated in [68], can be helpful in developing the DT prototype to access and verify safety
in HRC scenarios. Furthermore, DT combined with VR can be utilized for safely testing
the HRC scenarios [69]. A framework to integrate human–robot simulation with VR is
explained in [70]. SBA can initiate multiple DT instances to validate the design, safety
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and process planning. For example, an event-based simulation model of a DT with a case
study of an HRC scenario is discussed in [48]. This study focuses on work-load balances,
trajectory optimization and collision avoidance, and shows the capabilities of DT within a
HRC scenario. Hence, DT modelling can enable companies to save time and cost in the
design of robotic processes [71].
An architecture to implement DT for the controlled flow of information in a smart
CPS for planning is discussed in detail in [57]. Here, the foundation level is comprised of
the general physical properties of the product, the second level is the interface between
the machine and environment, the third level is the reaction or response system, and
the final level is the planning. This type of model-based simulation architecture can
assist in the preparation of shared tasks between humans and robots in HRC to increase
overall performance. The microservice architecture can also be utilized for the modular
development of DT-HRC using SBA [72]. A summary of additional articles related to SBA
and their application is summarised in Table 2.
Table 2. Summary of strategies and technologies that can potentially be used for building a simulation
based DT.
Strategies and Technologies
Digital thread based approach
Conceptual
Frame Work
Use Case
[23,59]
Implementation/
Demonstration
[60]
Hardware in the loop simulation
[63]
[68,73]
[61,62]
Software in the loop simulation
[68]
[74]
[66,67,69,75,76]
AR and VR
Data-oriented approach
[69,70]
[65,77]
DT of manufacturing process
Digital factory
[58,68,73]
[62,69]
[66,73]
[76]
HRC
[63]
[69,70]
Sensor technology
[58]
[69]
Task planning
[73,74]
[70]
3.3.2. Layer Based Approach
The second category, the layer based approach (LBA), describes the design of a DT
system generally consisting of three layers [14,21,53,78]. These three general layers are
the physical layer, virtual layer and connection layer/information processing layer [79,80].
Figure 6 provides a conceptual overview of modelling the DT-HRC using LBA. The attributes of the physical systems are collected via sensors that are mapped to the virtual
system with the help of the connection layer. The connection layer enables the exchange of
data between the physical and virtual layers.
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Figure 6. Layer-based approach for modelling of DT in HRC scenario.
The three main layers in LBA are as follows [19,21,29,53,78]:
•
Physical Layer
The physical layer consists of information about the state, the properties of the system
and the properties of the environment, collected through various sources such as smart
sensors, product development software, resource management and planning tools,
networking tools and communication tools. For example, information obtained in the
physical layer includes:
#
#
#
#
•
•
Design—CAD/CAM, factory layout, drawings of all the physical parts in the
production environment;
Resources—inventory data, bill of materials, process plan, physical properties
of the product, simulation and modelling results;
IoT devices—smart sensors, PLC, SCADA, distributed control system (DCS), actuators;
Network—communication protocol, machine-machine interface, feedback and control.
Virtual Layer
The virtual layer consists of an information model of the physical object and a data
processing module [81]. The information model represents the structure of the physical
object. The data processing module collects and processes the data about the physical
object for real-time reconstruction of the physical object in the virtual world. Hence,
the virtual layer represents the digital environment that provides the virtual replica of
the object present in the physical layer. The advantage of the virtual layer is that it can
create multiple instances of the digital twin model. These multiple models along with
the data acquired from the real world can be used to simulate, predict and optimize
the process. Additionally, the virtual layer can interact with its physical counterpart to
update the system parameters to increase the efficiency of the physical system.
Connection Layer
The connection layer acts as a bridge to link the physical and the virtual layer, i.e.,
it provides an exchange of data between the real-world entities and the digital twin
model. This layer handles data storage, processing and communication. The con-
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nection layers serve to map the data from the physical layer to the virtual layer and
vice versa. The changes can be updated automatically in the physical or in the virtual
model. Another purpose of this connection layer is optimization by processing the
collected data. The raw data obtained via the sensors are processed to extract relevant information from them. Therefore, various data mining and machine learning
techniques are used in the connection layer for process planning, optimization and
tracking changes [82].
An improvised model consisting of five layers, i.e., two more layers, namely data and
service, on top of the aforementioned layers has been proposed [21]. In this design, the data
layer is the core of the system, which interacts via the communication layer to the physical,
virtual and service layers in this 5-dimension system. A five-layer approach which is similar
to [21] is used in the design of DT for dual manipulator cooperation units [83]. Another fivelayer approach is created by dividing the layers into the physical layer, digital layer, data
layer, knowledge layer, and social layer [84]. Here, the social layer is similar to the service
layer in the 5-dimensional architecture previously mentioned. The service/social layer
integrates different services, such as customer relationship management, manufacturing
execution system and enterprise resource planning system. The knowledge layer integrated
with artificial intelligence capabilities is the brain of the DT. A five-dimensional architecture
framework of a reconfigurable DT model using the LBA discussed in [85,86] is similar to
that of the SBA. The architecture consists of four layers, namely the physical, model, data
and service layer, which are similar to the concept of SBA, namely design, engineering,
operations and service. Finally, an 8-dimensional model considering design elements such
as hardware, software, IT, computational model, information and intelligence with a focus
on intelligence, simulation and model richness is discussed in [87]. This 8-dimensional
model is for enabling intelligent DT systems for smart factory applications.
The growth of CPS has contributed significantly towards the realization of building
a DT model using an LBA [88]. The data required in the LBA can be grouped into two
categories, such as stationery and real-time data. The stationery or non-volatile data are the
information that is time-invariant, such as the mechanical design or CAD drawings, and
the real-time or time-variant data includes the updates received from sensors on dynamic
changes occurring in the shop floor environment [88,89]. An architecture for manufacturing
cells in Industry 4.0 that utilizes the IoT gateway, cloud databases and open platform
communication (OPC) to collectively combine and transfer data from the physical model to
its virtual counterpart is discussed [90]. Table 3 provides a summary of the literature that
utilises LBA based approach in various application.
Table 3. Different approaches within LBA using three are more layers.
Layer Based DT
Three-Layers
Five-Layers
Six or more Layers
References
[14,19,29,53,78–82,91]
[21,83,84,92–94]
[87,90]
3.4. SBA vs. LBA Development
The SBA focuses on the integration of various stakeholders in the product lifecycle
to share and exchange information at each stage [95]. Therefore, the development of
the SBA model is concurrent with each step of the product development from design to
engineering to operations and service. The information gathered at each stage of the product
development lifecycle can be reused when the product is revised or modified, thereby
facilitating modularity [23]. Hence, SBA is more likely to be suitable for new products that
are developed from scratch. The data from the initial stages of the design are stored and
carried over to create an entire lifecycle of the product with DT [49]. The DT model is tested
virtually using VC techniques to analyse the feasibility and cost before the actual start of
the physical commissioning of the manufacturing processes [96]. The recent developments
in the SBA enable the creation of a DT of existing machines, its reverse engineering for the
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identification of faults as well as for improving the condition of the machine. Such reverse
engineering techniques can be used in the development of DT of older industrial robots
to increase efficiency, accuracy and repeatability. The development of such a DT for an
existing robotic work cell by utilizing multi-level calibration is discussed in [97].
Although researchers follow both SBA and LBA methods, there is still a lack of clarity
on the development of the DT [14]. The development of DT in both SBA and LBA requires
information about physical entities, such as material properties, behavioral properties,
geometry, sensor attributes, and data management tools. Therefore, an LBA-based approach
is more suitable to model the DT of existing manufacturing shop floors. LBA can be used
for the development of a digital twin for an existing system which is monitored in real-time
and where there is access to the system parameters [98,99]. For example, the LBA of a shop
floor can be implemented by utilizing point cloud data from sensors or IoT devices. Such
devices can also capture human motion and utilize this data in the DT model [98,100]. The
modularity of SBA is favourable for the development of individual products or components,
which can be integrated later into a more extensive system such as a digital factory [101,102].
In a digital factory, the lifecycle of the entire system is monitored and tracked to provide
additional value to the operation [103]. However, the development process of DT for
individual products itself is intense and requires a lot of effort [104]. For instance, the effort
to create the DT of a simple casting mould, which has mass and shape properties, shows the
difficulty in creating the DT due to non-repeatability inherent with casting [105]. Similar
problems may be faced while creating the DT of a manufacturing process. Manufacturing
companies often outsource components or sub-assembly manufacturing to third-party
suppliers. In such cases, the suppliers should be equipped with the tools necessary to create
a DT for the component or sub-assembly being made. The data generated at this stage in
the supplier has to be sent back to integrate with the product data. This can be one of the
challenges for SBA.
Figure 7 suggests a conceptual framework involving SBA and LBA for developing the
DT of a collaborative robot.
•
•
•
•
For instance, in the development of a collaborative robotic arm, the information
pertaining to each of the joints is gathered, including the motor performance data, efficiency, motor velocities, acceleration, torque, data from force/torque sensors, system
information and diagnostic data. As the product is developed, the data at each stage
of product development are gathered and stored. These parameters can be updated in
a virtual model as its initial parameters. In this situation, the SBA can be used during
the development phase of the robot arm.
Likewise, pre-existing sensors along with additional sensors can be utilized so that the
robot may send information about its state to the virtual model. These exchanges of
data can be linked together by the data layer, as discussed in the LBA.
A cloud-based software tool containing the DT model with the actual parameters of
the robot can be provided by the OEM such that the end-users can connect the real
robot to the virtual model and monitor the DT. This could be useful in a series of tasks
including tracking, monitoring and parameterising the robot in order to increase the
accuracy, repeatability and life of the real robotic arm.
Furthermore, an application programming interface (API) can be used to extend
the scope of the DT of the collaborative arm across different simulation platforms
to create a digital factory environment. Developing the DT concurrently with the
development of an actual product can enable faster integration of models to build a
digital factory environment.
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Figure 7. Conceptual SBA and LBA for DT of the collaborative robotic arm. The developed DT can
be a part of the digital factory environment.
A conceptual framework highlighted the three main aspects of a HRC, namely awareness, intelligence and compliance [20]. This concept involves the perception of the environment, monitoring, path planning, trajectory optimization, object recognition and
gesture/speech-based control to build safety and trust between the human and the robot.
This framework, tested using simulation software allowing collaboration between the
human and the robot, can be a very useful method to evaluate the performance and trust
of HRC-DT during its development phase. Therefore, a SBA based approach can be used
for the training, evaluation and optimization of DT-HRC. To model DT-HRC using SBA,
a model of the robot, a model of the human, a model of the environment, and a model
of the operation/activities performed on the shop floor environment is required. The
next step in the development of DT-HRC is to acquire data from sensors to monitor and
provide feedback about the operator, environment and process. Therefore, it is essential to
develop a system that monitors the real-time attributes of the shop floor and react to the
non-deterministic nature of the environment [98]. Visual computing techniques can be used
for obtaining partially simulated feedback in real-time about the physical environment
by making an operator virtually interact with the collaborative robot [106]. Speed and
separation of the operator in an actual scenario can be monitored in real-time and fed
back to the virtual environment by using time of flight cameras/sensors attached to the
robot [107]. Such time-of-flight sensors can also be used to improve the safety, productivity
and performance of the HRC. However, there is no generic architecture in terms of building
a DT model [14]. The frameworks used for the development of DT are mainly depended
on the requirement and the type of the application.
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3.4.1. Software for the Development of Digital Twin for HRC
It is expected that both commercialized software tools and open-source platforms will
be used for the development of the DT of a given system. A number of the commercial
tools available to create a virtual replica of a robot carrying out the work as well as simulating the same in the virtual environment include Visual Components (VC), Siemens
Simatic WinCC and KUKA.Sim Pro [108]. In addition to these tools, game engines, such
as Unity3D, have already been used to develop the DT of industrial robots. Here, the
motion between the real and the virtual robot has been synchronised and tested [109].
Likewise, tools such as those found in Siemens Tecnomatix are used for simulation of
the DT model for optimization, ergonomics, assembly line planning, offline robotic programming and task planning [1,14,22,48,110]. DT using MATLAB is discussed in [111],
where the DT of an industrial robot coupled with machine learning algorithms is used to
monitor the workspace effectively and avoid collision so that the robotic cell is suitable
for HRC applications. Similarly, the DT of an industrial robot using machine vision for
autonomous decision making for sorting applications is discussed [112]. This approach
uses two packages, Robot Operating System (ROS) and MATLAB, for communication, data
handling and motion planning.
Two platforms that are frequently used for experimentation in DT are Gazebo and CoppeliaSim. These two simulation tools can be used with MATLAB and ROS, which provides
more flexibility, particularly in modelling humans in the virtual environment [20,98,113,114].
Cloud computing can be used to meet the high computational power required for DTHRC, as well as to take advantage of readily available cloud-based data processing
techniques [115]. Public cloud platforms or open-source cloud platforms, such as OpenWhisk, WSO2, or OpenShift can be used in such instances. However, cloud-based DT
can cause communication delays. Hence, these cloud-based solutions can be helpful in
processing the non-real-time data and for other non-real-time applications. A framework
for integrating DT and web-based technologies is explained in [116].
There are advantages and disadvantages of using open source and commercial software tools for the development of DT-HRC. The cost of the software must be considered
when selecting commercial tools. Some commercial software tools offer free licenses valid
for a specified trial period for users to carry out initial experiments. Another factor to
consider with any software is the learning curve. Commercial tools usually provide standard documentation to get started with the platform. However, to utilize the software to
its full potential, the user might have to attend dedicated training delivered by the company, which may incur an additional cost. Another advantage of the commercial platform
is the availability of dedicated customer technical support that can assist the user with
any technical issues when using the platform. On the other hand, open-source platforms
have a very steep learning curve with limited documentation for accessing advanced software capabilities. Some open-source platforms have online communities with supportive
users, which can provide quicker resolution to technical problems compared to commercial
software tools.
Thirdly, the range of functionality offered by commercial software tends to be better
than open-source platforms. Some commercial tools have a library of robot models, grippers
and other devices that can be easily imported into the virtual environment. However, tools
such as Gazebo require 3D models that are not readily available to be introduced into
the simulation environment. In comparison, CoppeliaSim offers some readily available
models within the software that can be easily imported into the simulation environment.
Commercial platforms also offer various built-in tools for data analytics, such as plotting
the joint parameters of the robot [108]. These features can be obtained in open-source
platforms but must be programmed to visualise the plot.
The final concern regards the transferability and modularity of the DT model. Use of
commercial software tends to restrict the ability to import the data from one platform to
another. This can be a potential bottleneck in the development of a complex DT model for
the manufacturing process involving several partners. The development of the DT model
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in an open-source platform can potentially avoid this problem. However, some companies
may be reluctant to use non-commercial or open source platforms due to cyber-security
and data protection concerns. In addition, there is the issue of computer operating system
compatibility, with DT software such as Visual Components, KUKA.Sim, and Siemens
Tecnomatix only available on the Microsoft Windows platform.
Example of DT-HRC Simulation vs. Visualisation
For many years, the open-source platform ROS has played an important role in
developing various robotic applications and has been widely used in HRC scenarios. For
example, a ROS-based architecture of coordinated assembly tasks involving a human and
a robot is presented in [117] and a DT involving a dual-arm collaborative mobile robotic
platform based on ROS is shown in [118].
There are various open-source or cost-efficient simulation software packages that are
compatible with ROS, such as CoppeliaSim, Gazebo, RViz, Unity3D and Blender. Some of
these platforms are game engines rather than a simulation environment, but can be used to
perform simulation tasks. An example of such an approach has been shown in [109]. A
combination of Blender and Unity3D was used to create a DT for a use case to optimize
the planning and commissioning process of a production process [119]. While most of
these software tools require additional plugins or packages for set-up and visualization,
RViz and Gazebo are widely used due to their compatibility with the ROS communication
framework. Therefore, for DT-HRC with ROS, simulation and visualization should be
considered as two separate tasks.
In order to visualize robotic simulation in RViz or in Gazebo, the model of the robot
needs to be imported in Unified Robot Descriptive Format (URDF) or in Xacro (an XML
macro language) format. In terms of robotic simulation, each of the joints in the URDF
are interconnected as revolute or prismatic joints depending on the type of robot. This
facilitates the rotation of each of the individual joints as a single degree of freedom (DoF).
However, there are no standard URDF or human mannequins available for RViz or Gazebo.
Capturing the motion of operators in real-time using sensors, such as IMU or vision system
in Gazebo or RViz is very complex [45]. This is mainly due to the issues related to the
creation of floating joints which are typically six DoF joints. The creation of such joints
is not supported in Gazebo or RViz. Multi DoF motion cannot be performed without
floating joints [120]. Hence, real-time operator tracking in RViz using the multi-DoF URDF
model is complex. This is one of the significant drawbacks of real-time monitoring and
simulation of human motion in ROS. An approach to overcome this limitation and visualize
the operator’s real-time motion may be the digital twin of a human avatar created using
open-source platforms, such as MakeHuman, Blender and RViz.
An overview of the proposed approach is shown in Figure 8. Here, a human model is
created using the open-source platform MakeHuman. This model is then imported into
Blender in STL CAD format. The human model is sliced into several parts, as shown in
Figure 9, using Blender to create a URDF model. This model is then imported into RViz
in the ROS environment for visualization. The proposed approach also involves multiple
Azure Kinect sensors, which provide real-time tracking information about the operator.
Sensor fusion approaches were used to combine data from these Azure Kinect sensors. The
fused data are then used to control the human model in RViz, creating a digital replica
of the human that responds to the real time motion of the actual human. ROS provides a
package called TF frames, which is used for one-to-one mapping of human joints to the
joints of the human model created in RViz. This package facilities the tracking of multiple
co-ordinate frames/joints. The body tracking information from the vision system published
as MarkerArray (dots shown in Figure 10) is converted and published as TF frames. The
number of joints published as TF frames corresponds to the exact number of joints present
in the URDF of the human model. Furthermore, the joint name published as TF from the
camera should be the same as the human joint’s name in the URDF to enable the tracking.
So, when the body tracking data and the URDF are launched, these are synchronized and
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the human motion in real-time can be used to visualize the actions of the operator in RViz,
as shown in Figure 10.
Figure 8. Process of creating a human model for visualisation in RViz.
Figure 9. Overview of the model used in RViz for visualisation. (Left) shows the skeleton and joint
information obtained from the Azure Kinect Camera. (Right) A human model developed using
MakeHuman and sliced into various parts using Blender. The pictures show the sliced region of the
human model used for visualization in RViz.
The section above describes real-time visualization of an operator in RViz. A method
involving ROS and Gazebo for human motion simulation for HRC task validation is
discussed in [121], where the human motion data are obtained from proprietary software.
These motion data are then captured and exported as Biovision Hierarchy (BVH) format,
which is used to control the human model in Gazebo. This method facilitates the validation
of the HRC process.
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Figure 10. Visualization of the operator (Operator Digital Twin) in RViz. The dots show the MarkerArray published from the vision system.
3.4.2. Deployment and Complexity
In HRC, testing, validation and risk assessment are crucial prior to the deployment of
collaborative robots onto the shop-floor alongside operators. The development of VR and
AR within the smart manufacturing paradigm enables visualization and interaction with
the objects present on the shop floor. The use of the virtual environment, including the uses
of AR and VR are explored in [42,122–128] for optimizing the real-world model to support
the operator with safety and task flow information. Virtual commissioning in SBA supports
the prediction of the operation time and the potential challenges in the real scenario [109].
Therefore, during the VC phase, simulation-based DT coupled with AR/VR technology
can assess the safety, impact and risk involved during the actual deployment. This ensures
that the system meets the required standards of the factory environment before being
deployed [112,122,129]. Several other methods, such as the virtual fence method [123]
and remote HRC technique [130] are also being investigated to improve the safety of the
operator during the development of DT.
Additional complexity arises with both SBA and LBA related to the flow and integration of data obtained at various stages. The deployment of DT-HRC depends on the
acquisition, integration and communication of data from various input modules. Therefore,
there are challenges with the integration of various pieces of information across multiple
heterogeneous platform models while developing a large-scale DT model such as a digital
factory [131]. Data integration techniques developed for CPS have focused on offering a
fully interconnected factory environment by connecting all the physical elements and the
necessary data. These techniques, such as those used in digital thread, can be useful as a
reference in building reliable data integration modules in DT [132,133]. One such example
involving the integration of various software tools for a DT of a single robotic cell is shown
in [110]. More information on the issues and challenges faced during the development of
DT for complex process are discussed in [134]. Here, an architecture framework to build,
control and communicate with the DT model is implemented in an industrial environment.
Software integration using the standard formats may not be feasible for a DT which is
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evolving over the product life-cycle [135]. If the development of the DT is based on a
standard format, software changes and asset modifications may become cumbersome.
Hence, reconfigurable modelling approaches can be used for making the DT adaptable to
frequent updates in products, devices, systems and technology [85].
In the future, digital thread can be used to overcome the difficulties with the integration
of multiple platforms in the DT-HRC. This can be achieved by a unified data flow that
can enable flexible cross-platform communication within the factory shop-floor models.
This can also be used as feedback to optimize the performance of the model [60]. Digital
Thread, as explained earlier, can provide faster historical and real-time information of the
DT model [60].
3.5. Benefits of DT-HRC
3.5.1. Safety
A critical challenge with DT-HRC is to develop a safe environment for the operators to
work with the robots as well as to create a sense of trust in order to work together flexibly
as a team [22,34,136,137]. The literature which focuses on modelling the human aspect of
HRC in DT is comparatively smaller than that which focuses on modelling the DT of the
robot [17]. However, in recent years, modelling of the human aspect within the DT has
become a more widely researched topic [100,138,139].
In HRC, the interpretation of human motion is challenging. Additionally, the dynamic
nature of the shop floor increases the complexity associated with modelling of the collaborative environment [140]. A safe working environment for human operators is essential in
HRC. This can be achieved by having feedback that gives an estimate of the approximate
positions or the intentions of the operator by using machine learning and AI algorithms combined with non-invasive sensing methods [44,46,47,140–142]. DT-HRC enables machine
learning-based approaches to ensure safe HRC via simulation models without altering the
physical environment [111]. In this regard, the cognitive symbiotic communication model
has recently gained attention. There are a number of other techniques used in HRC for
safety purposes. Methods such as voice, gesture, haptic and brainwave perceptions are applied to prevent human–robot collision and to carry out collaborative tasks efficiently [143].
For example, a force-based feedback system is used for the virtual verification of hand
guidance control of a collaborative robot in automobile applications [144]. Similarly, vision
sensors are widely used with HRC applications to monitor and track the operator and to
lower the impact velocity [145]. Additionally, vision-based safety systems can assist in
predicting human motion to effectively avoid a collision, optimize the trajectory of the
robot, or alert the operator [146,147]. Gesture-based control provides a safe method of
interaction between the collaborative robot and the operator [148]. Here, the movement of
the operator’s arm is tracked to identify the commands from the operator to carry out tasks.
Surface-pressure based standing posture recognition system combined with vision-based
techniques and deep learning can be utilized to detect the operator’s posture and predict
the operator’s intended action [149].
One advantage of DT-HRC is to effectively validate multiple techniques used in
the collaborative scenario and to perform a risk assessment on the model so that the
performance of the HRC is optimized before physical implementation on the shop floor.
The safety standards formulated for the collaborative scenario determine various factors,
such as the minimum safe distance between the human and the robot. The robots are
required to take a safe route while working near the operator to ensure the safety of
the operator [150]. A kinematic control strategy proposed focuses on the safety of the
operator as well as ensuring maximum productivity [151]. Metrics, such as minimum
separation distance used can be utilised to compare the results of the DT prototype and DT
instances [151]. This strategy can help in optimising the trajectory of the robot so that the
robot maintains a safe distance from the operator.
Safety is considered one of the crucial aspects of HRC. As mentioned earlier, it is
essential to develop a sense of trust between the human and the robot when tasks are
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carried out in a shared workspace [136,137,152]. However, the level of safety and trust in
a collaborative environment is determined by the programmer who programs the robot.
One challenge is the trade-off between robot velocity and safety in the HRC environment.
This trade-off is illustrated in Figure 11. The higher the velocity of the robot, the quicker a
task can be done, but this leads to lower trust and higher risk for the operator. The DT can
be used to find an optimal trade-off between creating a safe environment and minimizing
the task cycle time. Reducing the velocity of the robot could potentially stop the robot
from being operated at its maximum utility. However, in HRC, the safety of the operator
is the prime concern [153]. Hence, for bringing humans and robots together as a team
to use their collective intelligence and abilities, both the robot and the human need to be
trained together [154]. For this, there is a need to bridge the gap between the way humans
and machines are trained. DT-HRC can provide solutions to such problems in a much
faster way.
Figure 11. The trade-off between the safety and the velocity of the robot joint in HRC to benefit
both the production and safe HRC. DT-HRC can be used to create a model that complies with the
ISO guidelines as well as optimize the parameters of the robot in real-time. Various parameters are
continuously monitored and measured to meet ISO standards.
In summary, the DT model can be used to evaluate the risk effectively and comply with
the safety standards of HRC while optimizing the cycle time. The DT-HRC can evaluate
the safety of the system in real-time to monitor the key safety parameters and assist in
optimizing speed and configuration of HRC as per safety standards.
3.5.2. Maintenance
DT can support companies in machine health monitoring and maintenance activities.
DT models involve vast amounts of data that are collected and processed at various stages
to create a high-fidelity simulation model. These data include real-time data, historical
data, technical data and service data. The data can be processed to have a layer by layer
understanding of the operations, and to study the behaviour of machines in the long run.
The focus on the monitoring of machine health and subsequent maintenance planning using
data or cloud-based systems has increased due to the development of Industry 4.0 [155].
An approach that combines various predictive maintenance methods, such as statisticsbased reliability, physical model-driven and data-driven methods can decrease the error in
predictive maintenance [156]. Various sets of experiments can be conducted simultaneously
on the DT model to gather information to forecast the failure [157]. Likewise, corrective
maintenance using simulation-based DT utilizing the data collected during the engineering
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phase through production can potentially help minimize the production loss and machine
breakdown time.
Moreover, the timing of corrective maintenance can be highly uncertain [158]. Therefore, gathering information across different sensors in the DT model and using data mining
algorithms can provide more accurate system failure models and support human decisionmaking during maintenance tasks. For example, the data acquired from various sensors
and the benefits of cloud computing can assist the non-destructive testing and maintenance
of machines by providing real-time data for inspection instead of conventional intervention
methods. Therefore, data fusion and modelling is a core aspect of DT [159]. DT enables
manufacturing companies to focus on product diagnostics and root cause analysis for
product quality management [160]. A real-time DT that uses a probabilistic model of the
physical system can be utilized to diagnose the physical system [161]. This is achieved
by monitoring and comparing the DT and the physical twin in real-time. If the physical
twin’s behaviour deviates from the predicted behaviours modelled in the DT, then the
part in the DT that causes this deviation is further analyzed. Therefore, data from the DT
can be utilized to record the physical and digital footprint of the product throughout its
product lifecycle [133].
DT involves high fidelity graphical simulation environments that can provide flexibility through the visualization of the assembly of products on the shop floor so that there
is a minimum variation between the virtual model and the actual model. This type of DT
for geometry assurance has been discussed in the context of smart manufacturing through
the process of robotic spot welding [162], which enabled parts to be delivered with higher
quality. A framework for using DT for the quality inspection of components in a smart
factory scenario to measure the flatness of the component is also possible. The critical
technology enabling this is IoT and IoS (Internet of Services) for the creation of DT that
acts as a reference model [163]. Emulation software can be used alongside DT to improve
the functionality of old machines through reconditioning or retrofitting, a much cheaper
process than replacing the old machine [96].
3.5.3. Task Planning and Optimization
The contemporary nature of customer demand requires shop-floor environments to
be more flexible and customizable. HRC robots are highly suitable for reconfigurable
low-volume production facilities [164]. The rise in the number of HRC tasks within manufacturing facilities where humans and robots share a fenceless environment demands
methods to mediate tasks between them. There are various algorithms and decision support
systems for task planning and allocation in hybrid and collaborative work cells [114]. The
DT of HRC combined with information from the virtual shop floor, virtual world model
and the data from the physical world can be utilized to adapt the behaviour of HRC to
cope with varying production volumes [98]. The distribution of tasks in HRC will depend
on the shop-floor resources. Resource allocation can be formulated as a search problem
where an intelligent decision-making system distributes tasks to humans and robots separately [143]. An assembly planning framework that creates a DT of a product from its
digital description can perform the virtual assembly of the product and allocate resources
accordingly [165]. This approach, coupled with task planning algorithms, can be used to
automate the DT-HRC process to efficiently allocate resources and tasks separately to the
human and the robot working in a shared space. As the DT-HRC model receives data from
various IoT devices, it can continuously update its state and track changes. Furthermore,
data can be analyzed efficiently to improve the performance of the physical model [166].
The bottleneck of the process, cycle time and idle time for each task needs to be identified for
the adaptation of DT in process planning [167]. A discussion of planning and optimisation
of commissioning scenarios where a DT of a process is created is presented in [119].
A DT-HRC testbed implemented to analyze human-robot coordination shows how
information gathered across various modules that track human arm trajectory is used with
robotic reasoning to identify the tasks that the robot can do without colliding with the
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operator [168]. Another example of DT-HRC uses an event-based simulation to model
workload balancing, task distribution between the robot and the human, and trajectory
optimization as per ISO15066 [48]. It also shows the generation of an on-the-fly robotic
program to carry out the assigned tasks. Therefore, DT in HRC can be useful for accomplishing complex tasks and building a more robust, safe HRC environment. In addition,
DT-HRC enables simulation of the range of states of the robot and the operators as well as
the production scenarios that involve various resources to find an optimal solution [22].
3.5.4. Testing and Training
Maintaining a high level of human–robot engagement, enabling a skill-based task distribution between the robot and the human, the adaptation and validation of a workstation
layout according to the needs and virtual commissioning requirements of the production
system are some of the challenges in designing an HRC system [70]. During the deployment of the HRC system, testing, training and implementation are challenging tasks [122].
This problem is particularly acute when an industrial robot is being modified for collaborative tasks. DT combined with virtual reality (VR) could be a possible solution to this
problem [169]. Similarly, AR-based systems can be integrated for DT-HRC tasks [170].
VR is used today to test many CPS and to train operators [171]. In relation to combining
VR with DT, it should be noted that a combination of VR with discrete event simulation
has already been adopted in many industries [172]. Combining VR with DT will result in a
realistic system for operator training [169]. This combination has a series of advantages
when it comes to DT-HRC. A major challenge in DT-HRC is the difficulty in developing the
DT of the human operator. Combining DT with VR could partially solve this problem. The
technologies, software and protocols which are developed for VR could be used in DT and
vice versa. This could reduce the cost of developing the DT as the same technology could
potentially be used to provide VR solutions.
4. Challenges
Section 3 describes the types of approaches used in the modelling of DT, and compares
the benefits of the different methods of DT in relation to HRC. Despite the significant
potential for adopting DT-HRC in manufacturing processes, there are challenges to improve
the performance of DT. In many cases, DT can become a core component of manufacturing
technology, as it can potentially solve a range of problems and lead to the adoption of smart
manufacturing [21]. However, the question arises as to whether DT is a panacea for all
problems and how widespread the adoption of DT is. Much of the work to date on DT
focuses on its use as a model to predict the process and its outcome in the real world, i.e.,
as a simulation model [173].
The lack of a generic framework model or architecture for the development and
implementation of DT is a potential gap that hinders the growth of DT across various
applications [14,157]. There are currently no plug and play type modules available to
create a DT of any given system. An AutomationML-based concept for plug and simulate
provides a proof-of-concept for the automatic setup of the virtual environment by the
exchange of XML data from the plant simulation environment [174]. Here, the modular DT
interacts with the real-world setup to connect and transfer data, thereby supporting the
decision-making process.
Secondly, the lack of structured training programs on DT leads to a shortfall in the skills
and knowledge of engineers and developers. Therefore, the lack of adequate knowledge can
lead to losses in time, energy and resources in developing a DT model. Hence, there needs
to be suitable training and numerous application models to prepare designers and engineers
to build the DT model. To achieve this, engineers need to be educated and provided with
hands-on experience on DT during their university education. University programmes
should promote the use of digital tools wherever possible and improve computer science
education in engineering courses, provided that it does not come at the cost of reduced focus
on the traditional, core coursework [175]. The challenge here is balancing the fundamental
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courses in the respective engineering disciplines and the addition of DT computer sciencerelated courses to the curriculum. Strategies for restructuring the courses in engineering
programmes should be carried out in view of this potential problem. One way to do this
would be to add elective courses related to robotics and DT technologies.
A third challenge relates to the cost of development. The design and simulation of
the DT model requires investment in terms of cost, plant downtime and person-hours to
implement and deploy DT at various system levels [176]. Furthermore, there are multiple uncertainties involved in creating the model of the system, real-time feedback and
interaction between the physical and virtual models.
The success of a DT-HRC relies heavily on how well the HRC system is digitally
modelled. One key challenge related to creating the virtual model is that many physical
phenomena did not have a good digital model until relatively recently. For example, studies
related to the effect of force on materials (fracture, deterioration, etc.) and structures are
still in the research phase [56]. Without progress in such fundamental fields, it will be an
extremely difficult task for engineers to model the whole system and collect and interpret
the data from different sensors. It is expected that Artificial Intelligence (AI) can be utilized
to create virtual replicas or virtual models using the data collected from exteroceptive
and interoceptive sensors in the HRC system. In such cases, standardization of the data
format needs to be carried out in view of DT technology [88]. However, AI is also at the
development stage. It is predicted that high-level machine intelligence could be achieved
as early as 2040 [177]. For a complex HRC system, creating a DT that satisfies the standard
definitions will be difficult. Another issue to consider in the modelling process is the
challenges associated with the validation of the models.
The number of possible states the DT-HRC can take at a given time, considering the
dynamic nature of the human who is collaborating with the robot is also an obstacle in using
the DT-HRC for process optimization. The large number of parameters that affect these
types of interactions will increase the number of possible states of the system [56]. There
can be thousands of extrinsic and intrinsic parameters that can affect the HRC scenario.
Finding all these parameters and simulating the same conditions in the virtual world
will be tedious and computationally expensive. The modelling of a human on the shop
floor to accurately replicate an exact digital version of an operator in the virtual world is
extremely difficult to create. Modelling the digital twin of an operator, incorporating factors
related to physical health conditions and emotional aspects, such as trembling, fatigue and
emotional distress, to predict the outcome of the activities of the operator is very complex
and almost unfeasible, at least, with currently available technologies. Therefore, it is highly
unlikely to model the process in DT-HRC utilizing the complete model of operators in a
virtual environment.
The number of productive person-hours required to create a DT for simple robot
applications, such as a pick and place task, is still unclear. One example is the development
of DT that can mirror the exact robot in the virtual world, using a standard robot [83].
This DT provides a set of functionalities, such as plotting the robot data (current, velocity,
torque, and motor temperature). However, there are no studies on how this information
can be transferred to develop a DT of another robot or on how to model a more complicated
process. Moreover, most articles discuss creating a high-fidelity model using DT, but are
not clear on how the model should be developed. Likewise, the cost of purchasing and
using commercial process simulation software with the required toolbox or plugins to
create a DT is expensive. There is still a lack of knowledge and understanding about the
concepts of DT, which makes it more challenging for manufacturing industry to implement
DT concepts [26].
Fourthly, the integration of DT involves communication and interactions with various
CPS and IoT sensors, and therefore requires reliable sensor information and faster updates
of real-time data [178]. A potential challenge for implementing the DT-HRC is the problem
related to real-time connectivity and synchronization. The lack of high-fidelity models
in the virtual environment, along with the other challenges described previously, does
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not favour the possibility of establishing a closed-loop synchronization of the real and the
virtual model. This increases the difficulties of capturing and processing large amounts
of data. A reference model or a standard benchmark model is required to overcome these
challenges [49]. An attempt to create a DT of a 3D printer showed a delay of 2–3 s to
process the data and a 2-s delay in updating the digital and physical model [179]. These
authors have also mentioned that their system is much simpler with limited data, and it
will be much more difficult to synchronize a complex system. Another important issue
to be noted from this research is the 2–3 s computational time required to process the
data collected for a system with limited data [159]. This delay is not suitable for realtime control. In another example, the use of a DT to model crack propagation in a single
mechanical component having machined features underlines the requirement of high
computational power for DT [180]. It took four days to simulate the model when the
simulation was running on a high specification personal computer. HRC systems are
much more complex than the above-mentioned examples, with many assembled parts,
sensors, actuators and other components in a dynamic environment. It is expected that
the communication between the physical and virtual world will be much slower and the
computational requirements will be multiples of those mentioned in the above literature.
It has been reported that the synchronization and the convergence of the physical and
the virtual layer fail to create a detailed simulation model for ultra-high visual analytics
of the DT model [48,181]. There are various challenges regarding the use of data analytics
and IoT in the context of Industry 4.0 that need to be addressed first before moving on to a
photorealistic, fully functional DT of real-time entities and processes [182]. One issue with
the IoT and CPS is the compatibility of sensors to integrate old robots or machines. This may
lead to the need for new equipment to realize the objective of DT, thereby increasing the
cost of the process. As various sensors and modules interact, there is also a concern within
the industry regarding the security of networks to prevent cyber-attacks [183,184], leading
to another challenge with DT, which is about ensuring the secure real-time interaction
between various modules during the exchange of data [173].
Fifth is the accuracy and the precision of the DT simulation model. Currently, most of
the DT models create a virtual copy of robots by capturing their motion in the real world
and replicating the same action in the virtual world. DT offers the ability to implement
various algorithms that can run in parallel without affecting the real-world setup to find the
best parameters for optimal results. However, most of these fail to consider the modelling of
various complex processes within a DT model, such as wear and tear of the gripper fingers,
servo gears, effect of latency between the gripper and the controller and repeatability of the
robot over time. An example illustrated in Figure 12 shows a robot involved in gripping
an unevenly shaped object. An actual DT of this process must show the actuation of the
gripper, wear and tear of the gripper fingers over time, how the object is gripped, the effect
of deformities on the object due to gripping force and if the object falls while the robot
is motion due to less force. An accurate representation of the process, factoring in many
unknown variables, is required to create a highly accurate model of the virtual factory
environment [173]. Some of these details are less important than others and can be avoided
in a DT. However, the level of precision required of the DT compared to the real world is still
unclear. For instance, the DT for geometric assurance in welding focuses on a conceptual
framework model and the use of DT to minimize the geometric deviation between the
virtual and the real component by using real-time data for optimization [162,185]. However,
the optimization of a welding process in real-time is very complicated. It requires highspeed communication and data transfer between the real system and the virtual system. The
optimization algorithms should be coupled with Finite Element Method (FEM) software
to precisely analyse various parameters, such as ideal temperature, welding sequence
and welding positions. In addition, visually representing the outcome of the process
as accurately as seen in the real world will be impossible to achieve in the short-term.
Moreover, from an industry practitioner perspective, DT is still just used as a simulation
model, unlike the full functionality that the DT proposes to offer, with the main objective
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currently being just to gather data from the physical platforms to control the manufacturing
lines, rather than running a virtual simulation in parallel [173].
Figure 12. The complexity of modelling DT in robotics. It is still uncertain how DT can exactly
represent a real-world process such as wear and tear, physical damages, object deformities, object slip
and deflection of gripper finger.
Similarly, the fidelity of the physics engine used for simulation in the virtual environment is an issue, often termed as “the reality gap”. While there are many alternative
physics engines, all of which are constantly improving, realistic simulation is still a significant challenge. There are ways of offsetting this problem by using more than one physics
engine in tandem with one another [186], or the use of algorithms such as grounded action
transformation [187] and its variants [188,189]. The stochastic nature of the real world is
a particularly prominent issue here [188]. Simulating frictional contact between objects is
also a notable issue and this is relevant for robot grasping tasks [190]. For instance, it was
found that even small changes in initial conditions could significantly change end results
when simulating frictional tasks using common physics engines. These results suggest
that a trade-off exists between a simulation’s accuracy and its predictability [191]. The
performance of different models by simulating several different tasks involving friction
(grasping, sliding, wedging and stacking) was examined [192]. Simulating soft robotics is
also generally more challenging, particularly when combined with frictional contacts [193].
To deal with these challenges, displaying a confidence value alongside the simulation and
letting the user know when a simulation displays accurate results could be a prudent
approach [194]. Finally, the challenges around CPS and IoT, such as connectivity, computational power, interaction with various heterogeneous modules involving non-standard
communication protocol, may also prevent the creation of a high fidelity virtual model [195].
The adoption of DT across industries also will face challenges because of the design
of organizational structures [196]. The typical structure of any organization, including
manufacturers of HRC systems, is to have separate sections for research and development,
production, finance, marketing and human resources. Research and development could be
divided further into different sub-functions, such as design, engineering, etc. Each of these
units acts as a silo of information with very little crossflow of information. One department
might not use the virtual models and simulations used by another department in the same
organization because of the difference in their requirements. To realize the benefits of DT, a
unified view of the available information is needed, but internal organizational barriers
may hinder this and delay the adoption of DT technology.
The aforementioned challenges can result in a very high cost for implementing DT
outside the research laboratory [175]. While research on DT-HRC systems is at a very early
stage, it is suggested that the resources and expertise needed for DT will be accessible only
by large companies [197].
Though there is growth in the field of computing and other technologies contributing
to the development of DT, there is still a lack of clear understanding about what needs
to be represented in the virtual world. Furthermore, the lack of training modules and
materials on the development of DT adds more complexity and delays in building an exact
model. Common approaches and standards among researchers and practitioners need to be
developed regarding the expectation and purpose of creating the DT of a particular process.
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While digitalization is regarded as the key to the future of the manufacturing industry,
and is an area of strategic focus for many [108], most of the DT systems are built on the
technological growth and advancement of the Industry 4.0 paradigm [173]. Since it was
first introduced in 2011, the concept of Industry 4.0 has evolved and has played a crucial
role in the development of manufacturing technologies. Nevertheless, there are still many
challenges facing Industry 4.0 that remain unsolved [184,198]. Overcoming these challenges
will also benefit DT development.
Human–robot collaboration will play a vital role in manufacturing and assembly
processes. Various safety challenges need to be considered to enable a safe human–robot
interaction environment [153,199]. DT prototypes can assist with the safe planning of HRC
scenarios, while the DT instance can monitor and plan the movement of the robots to ensure
real-time safety feedback. However, the degree of fidelity of these simulation models in
HRC still lacks clear definition and clarity.
A number of reports suggest how DT can add value to a manufacturing operation and
how DT can help solve the problems faced in the manufacturing environment [200–202].
However, DT is a very long way from achieving what it is fully capable of doing. The
research reported in the literature either describes examples of different architectures that
can be used to build the DT, or demonstrates a DT which captures only a small part of the
physical process. Full-scale DTs, incorporating all the essential aspects of a system from
control to production management to the collaboration of an HRC employed in a shopfloor,
are yet to be developed.
The dynamic updating of the DT model relies on information from various points,
such as sensor data, controller data and management tools. 5G wireless network systems
that provide low latency and higher bandwidth for faster communication can potentially
overcome the challenges faced within CPS and IoT [203]. Therefore, it can contribute to the
advancement in DT-HRC technology for manufacturing processes [204,205]. Furthermore,
a 5G network coupled with high-performance computing (HPC) and simulation software
can potentially improve the fidelity of the DT model, thereby providing a more realistic
representation of the real-world model [206]. Additionally, this can enable access to the
smart factory paradigm to remotely monitor and control aspects of the real-world model.
5. Outlook and Conclusions
The growth of DT indicates its potential to be an integral part of HRC to support
safety, maintenance and task planning. In this paper, we have focused on the impact of
DT technologies in the manufacturing domain and various approaches used for creating
DT. The research trend shows that the DT is in the technology growth phase and will
take many more years to reach maturity. Research on the creation of DT of the industrial
robotic process is increasing exponentially, while that of DT HRC is lagging behind. This is
mainly due to the fact that it is much easier to create a digital model of an industrial robot
than a collaborative robot, due to the uncertainty pertaining to modelling the environment
involving the human operator. There are research efforts ongoing to demonstrate the DT of
simple robotic tasks with limited sensing capability without considering factors such as
ageing and the wear and tear of components. Running a real-time simulation and DT is
still a challenge.
Different modelling methods are available for creating a digital model of a task or a
process. The integration of models of such processes under one umbrella is essential to
create a realistic model of the physical system. Photo-realistic simulation in conjunction
with physics-inspired neural networks can be used for DT-HRC applications. In recent
years, the advancement of photo-realistic simulation environments such as Nvidia Isaac
platforms and physics-inspired neural networks could potentially overcome the limitation
faced in the area of DT-HRC. Process simulation and visualization platforms, such as Visual
Components (VC), Siemens Simatic WinCC and KUKA.Sim Pro, CoppeliaSim, Gazebo,
Siemens Technomatix, programming and computing platforms such as MATLAB and
communication frameworks such as ROS/ROS2 could be used for creating the DT of
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human–robot collaboration. Cloud platforms such as OpenWhisk, WSO2, or OpenShift can
be used for data processing.
In technologies related to AR and VR, such as real-time communication, the necessity
of realistic representation of the physical system and the environment are also essential for
creating a DT. Developments in these fields could bring radical changes in DT technology
and its implementation. Game engines are an option worth considering for the visualisation
of DT-HRC, with simulation and visualisation of DT-HRC considered as two different
aspects. An instance of the DT-HRC process can be created to perform simulations in the
background, while the visualisation through game engine tools may provide a real-time
update of the process.
There are various concepts and frameworks currently available to develop a DT model.
However, a greater focus on creating a generic approach that can serve as a common base
for all DT platforms could produce interesting findings as the technology matures. With
the advancement of Edge AI or AI-enabled hardware graphical processing units (GPU)
such as the Nvidia Jetson series or AI-accelerators such as Intel Movidius products, the
DT of a factory level process can be decomposed into smaller DT processes in a modular
manner. This allows the processing of information at the lower system level, rather than
utilising data at a higher level for parameterizing/tuning the operations. This can unlock
different new approaches for building a factory level DT.
The challenge and potential of DT within the manufacturing domain were also discussed in this paper. The challenges around CPS and IoT need to be addressed together
with advances in simulation environments with physics engines to achieve the expected
benefits of DT. There is also a need for better modelling of complex tasks to build more
accurate DTs. In addition, better data transfer between different modules of the DT is essential. Continued progress in the fields of communication and semiconductor processing
technologies are key enablers in this regard.
Furthermore, the notion and understanding of DT varies from person to person. Some
consider DT as a simulation, for others, it is an emulator that has all the precision of the
system being emulated. Therefore, there is no consensus on how to define or consider the
DT of a given system. In summary, several questions pertaining to DT technologies still
remain to be answered. The major challenges around DT are listed below:
•
•
•
There are no definite methods to determining the fidelity of the DT platform;
Various issues and difficulties associated with DT-simulation, as well as with the
modelling and visualization, still remain unsolved;
Lack of standard communication frameworks for handling the multitude of sensor
data, avoiding latency and allowing synchronisation to build a real-time DT of a
shop-floor.
Author Contributions: Conceptualization, N.P. and A.K.R.; methodology, A.K.R. and R.M.; software,
A.K.R.; formal analysis, R.M. and A.K.R.; investigation, A.K.R. and R.M.; resources, N.P.; data
curation, A.K.R. and R.M.; writing—original draft preparation, A.K.R., R.M. and M.K.; writing—
review and editing, V.H., R.M., A.K.R., N.P. and M.K.; visualization, A.K.R. and R.M.; supervision,
N.P.; project administration, N.P.; funding acquisition, N.P. All authors have read and agreed to the
published version of the manuscript.
Funding: This research is supported by European Union Horizon 2020 Framework Program—
SHERLOCK under Grant Agreement: 820689.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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