11institutetext: Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay,
France

# Coherent $\mathrm{J}/\psi$ photoproduction and polarization in peripheral
Pb$\\--$Pb collisions

Afnan Shatat for the ALICE Collaboration 11<EMAIL_ADDRESS>
###### Abstract

Photonuclear reactions are induced by the strong electromagnetic field
generated by ultrarelativistic heavy-ion collisions. These processes have been
extensively studied in ultraperipheral collisions, where the impact parameter
is larger than twice the nuclear radius. In recent years, the observation of
coherent $\mathrm{J}/\psi$ photoproduction at very low transverse momentum has
been claimed in nucleus$\\--$nucleus (A$\\--$A) collisions with nuclear
overlap, based on the measurement of an excess in the $\mathrm{J}/\psi$ yield
with respect to hadroproduction expectations. Such quarkonium measurements can
help to constrain the nuclear gluon distribution at low Bjorken-$x$ and high
energy. The photoproduced quarkonium is expected to preserve the polarization
of the incoming photon due to the $s$-channel helicity conservation. In this
contribution, we report on the new preliminary measurements of the
$y$-differential cross section and the first polarization analysis of
coherently photoproduced $\mathrm{J}/\psi$ in peripheral Pb$\\--$Pb collisions
with the ALICE detector at forward rapidity in the dimuon decay channel.
Comparison with models will be shown when available.

##  1 Introduction

A coherent $\mathrm{J}/\psi$ photoproduction observation has been reported for
the first time by the ALICE Collaboration in nucleus$\\--$nucleus (A$\\--$A)
collisions with nuclear overlap at very low transverse momentum ($p_{\rm T}$)
ALICE:2015mzu . Coherent vector meson photoproduction is a well-established
process in ultraperipheral collisions (UPCs), but its observation in
Pb$\\--$Pb collisions with nuclear overlap raises several theoretical
challenges, for instance on the survival of the coherence condition when the
colliding nuclei are broken by the hadronic interaction. This process is also
a valuable tool for studying the nuclear gluon distribution in the poorly-
known region of low Bjorken-$x$ Eskola:2009uj . The determination of
$\mathrm{J}/\psi$ polarization at low $p_{\rm T}$ is crucial in verifying the
origin of the observed excess yield. Due to the conservation of s-channel
helicity, the produced quarkonium is expected to preserve the polarization of
the incoming photon GILMAN1970387 . The coherent $\mathrm{J}/\psi$
photoproduction cross section was measured as a function of centrality in
Pb$\\--$Pb collisions with nuclear overlap ref:Photo_cent_5 . Reproducing the
collision-centrality dependence of the $\mathrm{J}/\psi$ photoproduction cross
section remains challenging for most of the available models, as the photon
flux and the photonuclear cross sections used as input to the calculations can
strongly be affected by the presence of a nuclear overlap. To further
constrain theoretical models, additional differential measurements are
required. For instance, a strong rapidity ($y$) dependence is expected from
models (see for example ref:Model_GBWIIM ), especially in the forward rapidity
region $2.5<y<4$.

##  2 Experiment and analysis

The ALICE detector is described in detail in ALICE:2008ngc . In the presented
results, the $\mathrm{J}/\psi$ is studied in the dimuon decay channel with the
ALICE muon spectrometer. The muon spectrometer consists of five tracking and
two triggering stations. An iron wall is located between the last tracking and
the first triggering station to filter muons. A front absorber is located
between the interaction point and the first tracking station, to suppress the
hadronic background. The cross section and polarization analyses are conducted
in Pb$\\--$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV in the
70$\\--$90$\%$ centrality range. The event and track selections are the same
as in the previous measurement ref:Photo_cent_5 . The used data sample has an
integrated luminosity of $756\leavevmode\nobreak\ \pm\leavevmode\nobreak\
19\leavevmode\nobreak\ \mu\rm{b}^{-1}$. The $\mathrm{J}/\psi$ yield is
extracted by fitting the opposite sign dimuon invariant mass distribution. The
polarization is measured through a fit to the dimuon angular distribution in
order to extract the three polarization parameters
($\lambda_{\theta},\lambda_{\phi},\lambda_{\theta\phi}$) in the helicity frame
where the $\mathrm{J}/\psi$ flight direction in the lab frame is the
polarization axis. The set of parameters
($\lambda_{\theta},\lambda_{\phi},\lambda_{\theta\phi}$) = (1, 0, 0)
corresponds to transverse polarization. A further explanation of the
polarization parameters and the fit function is available in ref:UPC_transvPol
.

##  3 Results and discussion

The $\mathrm{J}/\psi$ raw yield is extracted in six rapidity intervals at
forward rapidity $2.5<y<4\leavevmode\nobreak\ $ in Pb$\\--$Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV in the 70$\\--$90$\%$ centrality range. The
resulting $p_{\rm T}$-differential $\mathrm{J}/\psi$ raw yield is displayed in
the left panel of Fig. 1. The right panel of Fig. 1 shows the measurement of
the corresponding nuclear modification factor $R_{\rm
AA}^{\mathrm{J}/\psi}=(1/<T_{\rm AA}>)\times
dY_{\rm{AA}}^{\mathrm{J}/\psi}/d{p_{\rm
T}}/d\sigma_{\rm{pp}}^{\mathrm{J}/\psi}/d{p_{\rm T}}$, where $<T_{\rm AA}>$
and $Y_{\rm{AA}}^{\mathrm{J}/\psi}$ are the average nuclear overlap function
and the invariant $\mathrm{J}/\psi$ yield in A$\\--$A collisions,
respectively, while $\sigma_{\rm{pp}}^{\mathrm{J}/\psi}$ is the hadronic
$\mathrm{J}/\psi$ cross section in proton$\\--$proton collisions at the same
energy. The $R_{\rm AA}$ decreases as the rapidity increases and exhibits a
significant rise at low $p_{\rm T}$.

Figure 1: The $p_{\rm T}$-differential $\mathrm{J}/\psi$ raw yield
distribution (left) and the $\mathrm{J}/\psi$ nuclear modification factor
$R_{\rm AA}$ as a function of $p_{\rm T}$ (right) in Pb$\\--$Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV in the 70$\\--$90$\%$ centrality range, for
several rapidity intervals.

For $p_{\rm T}<0.3$ GeV/$c$, the $\mathrm{J}/\psi$ hadronic yield in
Pb$\\--$Pb collisions is estimated via a data driven approach that uses as
inputs the $\mathrm{J}/\psi$ hadronic cross section in pp collisions, the
$\mathrm{J}/\psi\leavevmode\nobreak\ R_{\rm AA}$, and the hadronic
$\mathrm{J}/\psi$ Acceptance x Efficiency ($A\times\epsilon$) in Pb$\\--$Pb
collisions. A significant $\mathrm{J}/\psi$ excess over the expected hadronic
yield is observed in the raw $\mathrm{J}/\psi$ yield distribution for $p_{\rm
T}<0.3$ GeV/$c$. The excess yield along with the rise in the $R_{\rm AA}$
indicates that photoproduction occurs at low $p_{\rm T}$. To obtain the
coherent $\mathrm{J}/\psi$ photoproduction yield, the excess yield is then
corrected for the fraction of incoherent $\mathrm{J}/\psi$ ($f_{I}$) and
coherent $\mathrm{J}/\psi$ from coherent $\psi{\rm(2S)}$ feed down ($f_{D}$),
taken from UPC measurements ref:UPC_fIfDvalues . In order to calculate the
coherent $\mathrm{J}/\psi$ photoproduction cross section, the coherent yield
is corrected for $A\times\epsilon$ assuming transverse polarization and
divided by the $\mathrm{J}/\psi$ branching ratio in the dimuon decay channel
and the integrated luminosity.

Several $\mathrm{J}/\psi$ photoproduction models in UPC were extended to
describe peripheral collisions. First, in the hot-spot model (GG-hs), the
photon flux is estimated in the same way as in the UPC case, but the integral
is limited to the impact parameter range of the selected centrality class
ref:Model_GGhs . Second, based on a picture of photon-pomeron coupling, the
Zha model proposes that one nucleus acts as a photon emitter and the spectator
nucleons in the other nucleus, which do not undergo the hadronic interaction,
act as a pomeron emitter ref:Model_Zha . Finally, the GBW/IIM model considers
an extension from the UPC calculations to peripheral collisions with three
distinct scenarios ref:Model_GBWIIM . The GBW and IIM stand for two different
calculations for the photon-target interaction within a dipole picture
framework. Scenario 1 (S1) has no relevant modification in relation to the
ultraperipheral regime. In S2, an effective photon flux is considered
including only the photons that reach the spectator region of the target
nucleus. In S3, the effective photon flux of S2 is used, and in order to
exclude the nuclear overlap region, an extra modification of the UPC
photonuclear cross section is considered. In Fig. 2, the $y$-differential
coherent $\mathrm{J}/\psi$ photoproduction cross section is compared to
several models. In peripheral collisions, where the nuclear overlap effect on
the coherent $\mathrm{J}/\psi$ photoproduction cross section is expected to be
small, some sets of models which include (Zha, IIM S2/S3) or not (GG-hs, GBW
S1) the nuclear overlap can both describe quantitively enough the overall data
trend (magnitude and rapidity dependence of the cross section). The current
theoretical calculations would need further constrain from UPC results
ref:UPC_sigma_y  in the first place, to better interpret the peripheral data.

Figure 2: The forward $y$-differential coherent $\mathrm{J}/\psi$
photoproduction cross section in Pb$\\--$Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV in the 70$\\--$90$\%$ centrality range, and
for $p_{\rm T}<0.3$ GeV/$c$, compared to GG-hs ref:Model_GGhs , Zha
ref:Model_Zha , and GBW/IIM ref:Model_GBWIIM  (left) and the three scenarios
of the GBW/IIM ref:Model_GBWIIM  model (right).

The dimuon angular distribution in cos$\theta$ intervals and for $p_{\rm
T}<0.3$ GeV/$c$ is presented in the left panel of Fig. 3. The polarization
parameter $\lambda_{\theta}$ is extracted from a fit to the angular
distribution and is compared to the UPC measurement in the right panel of Fig.
3. The parameter $\lambda_{\theta}$ is close to unity and is compatible with
the observed polarization for the coherently photoproduced $\mathrm{J}/\psi$
in UPC within the large uncertainties ref:UPC_transvPol . This observation
supports the scenario of a dominant coherent $\mathrm{J}/\psi$ photoproduction
contribution over the hadronic one, in the kinematic region $p_{\rm T}<0.3$
GeV/$c$ and $2.5<y<4\leavevmode\nobreak\ $, in peripheral Pb$\\--$Pb
collisions ref:Photo_cent_5 .

Figure 3: Inclusive $\mathrm{J}/\psi$ cos$\theta$ distribution in the helicity
frame for $p_{\rm T}<0.3$ GeV/$c$ and $2.5<y<4\leavevmode\nobreak\ $ in
Pb$\\--$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV (left). The
polarization parameter $\lambda_{\theta}$ extracted from the left plot is
compared to the coherently photoproduced $\mathrm{J}/\psi$ $\lambda_{\theta}$
polarization parameter in UPC ref:UPC_transvPol  (right).

##  4 Conclusion

We have reported the first $y$-differential coherent $\mathrm{J}/\psi$
photoproduction cross section measurement along with the first inclusive
$\mathrm{J}/\psi$ polarization measurement, for $p_{\rm T}<0.3$ GeV/$c$ and
forward rapidity, in peripheral Pb$\\--$Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=5.02$ TeV with ALICE. The cross section measurement
was compared to several models that can reproduce the magnitude of the cross
section, but fail at reproducing the $y$-dependence, similarly to the UPC
case. The existence of coherent $\mathrm{J}/\psi$ photoproduction in
Pb$\\--$Pb collisions with nuclear overlap therefore remains a theoretical
challenge. Extending the cross section measurement to the most central
collisions can serve as a discriminator of the proposed models. The
polarization parameter $\lambda_{\theta}$, measured in the helicity frame, is
compatible with transversely polarized $\mathrm{J}/\psi$ and is consistent
with the measurement of the coherently photoproduced $\mathrm{J}/\psi$ in
UPCs. A larger Pb$\\--$Pb data sample of about $6\leavevmode\nobreak\
\rm{nb}^{-1}$ integrated luminosity is expected to be collected during LHC Run
3. This will allow one to obtain a significant signal of coherently-
photoproduced $\mathrm{J}/\psi$ in the most central collisions and to improve
the precision of the cross section and polarization measurements. In addition,
it will permit the examination of other coherently-photoproduced vector mesons
to investigate potential QGP effects on these photoproduced probes.

## References

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2023

[1]Lin Xie

[1]University of Twente, Hallenweg 17, Enschede, 7522NH, The Netherlands

2]Beijing Hanning Tech Co., Ltd

# How to Integrate Digital Twin and Virtual Reality in Robotics Systems?
Design and Implementation for Providing Robotics Maintenance Services in Data
Centers

<EMAIL_ADDRESS>Hanyi Li<EMAIL_ADDRESS>*  [

###### Abstract

In the context of Industry 4.0, the physical and digital worlds are closely
connected, and robots are widely used to achieve system automation. Digital
twin solutions have contributed significantly to the growth of Industry 4.0.
Combining various technologies is a trend that aims to improve system
performance. For example, digital twinning can be combined with virtual
reality in automated systems. This paper proposes a new concept to articulate
this combination, which has mainly been implemented in engineering research
projects. However, there are currently no guidelines, plans, or concepts to
articulate this combination. The concept will be implemented in data centers,
which are crucial for enabling virtual tasks in our daily lives. Due to the
COVID-19 pandemic, there has been a surge in demand for services such as
e-commerce and videoconferencing. Regular maintenance is necessary to ensure
uninterrupted and reliable services. Manual maintenance strategies may not be
sufficient to meet the current high demand, and innovative approaches are
needed to address the problem. This paper presents a novel approach to data
center maintenance: real-time monitoring by an autonomous robot. The robot is
integrated with digital twins of assets and a virtual reality interface that
allows human personnel to control it and respond to alarms. This methodology
enables faster, more cost-effective, and higher quality data center
maintenance. It has been validated in a real data centre and can be used for
intelligent monitoring and management through joint data sources. The method
has potential applications in other automated systems.

###### keywords:

Digital twin, Virtual reality, Robotics services, Autonomous mobile robots,
Data center, Monitoring, Maintenance

##  1 Introduction

We are witnessing an accelerated development of new and emerging technologies
(e.g. mobile technologies, virtual reality, digital twins) in the world of
Industry 4.0, where the physical and digital worlds are closely linked. Today,
thanks to advances in sensor technology, professional service robots are
growing rapidly (by 131,800 units (+41%) and US$6.7 billion worldwide (+12%)
in 2020; see rotstatistics ). According to the International Organization for
Standardization, a service robot is defined as “a robot that performs useful
tasks for humans or equipment, excluding industrial automation applications”
(defrobot ). Unlike industrial robots, service robots can operate in
unconstrained everyday environments. The International Federation of Robotics
has published the top five applications for service robots in 2020,
particularly autonomous mobile robots (see rotstatistics ; we will refer to
them as mobile robots in the rest of this paper). Most of them will operate in
indoor environments for production and warehousing (see, for example, an
overview of automated warehouses in azadeh2019robotized ; xie2021introducing ;
xie2022formulating ).

Automated systems are usually very complex. Before bringing such systems into
reality, simulation is widely used as a virtual model to test the
functionalities of the physical assets in such systems, e.g. to test a robot’s
navigation and localization algorithms (as in merschformann2017rawsim  for an
automated warehouse), or to test different solution approaches for different
problems in such systems to maximize performance (as in xie2021introducing
for an automated warehouse). The increasing development of sensors and the
Internet of Things (IoT) allows the virtual model to interact with the
physical system throughout its lifecycle. A digital twin (DT) is a dynamic and
digital representation of a real object or system. Over the lifetime of the
object, the digital representation is dynamically updated based on data
provided by its physical counterpart. A more abstract definition of the DT can
be found in van2021resilient , while various definitions are listed in
lim2020state . Nowadays the DT is considered as a model to be used as a basis
for simulations, analyses, etc. (dietz2020digital ). An example of a
simplified DT for an automated warehouse can be found in xie2018simulation
and barosan2020development . A DT of a system is expected to save time, money
and effort by helping to identify system failures before they occur in the
real system. DTs are widely used in industries such as product and
manufacturing design, automotive (electric vehicles), predictive maintenance,
healthcare, and so on. A recent review of various applications, especially in
electric vehicle drive systems, can be found in ibrahim2022overview  and
niaz2021autonomous .

DTs have recently been used in automated systems by engineering researchers to
test localization algorithms, robot motion (lumer2021towards ) and safety
(almeaibed2021digital ), and the operating environment (e.g., warehouse layout
options in stkaczek2021digital ) in a virtual environment before they are
implemented in the real object. A recent overview paper on DT and its future
perspectives can be found in lim2020state , while a literature review on DT
applications in manufacturing can be found in cimino2019review .

Although there is a trend for mobile robots to become increasingly autonomous
(alatise2020review ), there are still some situations that require
teleoperation by human personnel, e.g. in hazardous and dangerous environments
where unexpected robot behavior must be avoided. Virtual Reality (VR), thanks
to its natural acting capabilities, is a well-adapted means to help a human
interact with a robot remotely. VR is a representation of reality and it
simulates a virtual environment that immerses the user to the extent that he
has the feeling of being there (see wohlgenannt2020virtual ). Feedback from
the environment is provided through images, sound, sensory stimuli, and
interaction. More on the definition of VR and the use of VR in practice and
research can be found in wohlgenannt2020virtual . According to roldan2019multi
, VR is suitable when personnel are away from the mission, the mission is
dangerous, the scenario is large, or personnel have to manage many resources
(e.g. multi-robot systems). We need this technology especially when personnel
and robots can interact with the real and virtual objects of the scenario. VR
has been widely used in manufacturing, from design and training, programming
and process monitoring to maintenance (oyekan2019effectiveness ). More
recently, VR has been used to teleoperate mobile robots, e.g. in
kot2018application  for remote military use and in li2022towards  for
inaccessible remote environments, while there is an overview of applications
in engineering research projects in wonsick2020systematic , e.g. on
controlling and interacting with robots (excluding mobile robots).

VR provides a safe and cost-effective environment for testing concepts and
hypotheses, such as a robot arm, before deployment in the real world. In
addition, the feedback, knowledge, and optimized virtual models obtained in a
VR environment can be transferred to the real-world object. To achieve a
seamless transfer, a DT of the real-world environment, including the workshop
and the robot arm, is required.

###  1.1 Contribution and structure of the paper

The combination of DT and VR technologies has recently been involved in
engineering publications, mainly on the design of human-robot collaboration in
an industrial manufacturing process (oyekan2019effectiveness ;
havard2019digital ; perez2020digital ), the methods for programming and
controlling robots (burghardt2020programming ; kuts2019digital ), a laboratory
application in a circular economy (rocca2020integrating ), the calibration of
mobile robots (williams2020augmented ), or safety training
(kaarlela2020digital ). These have been isolated efforts with no guiding
principles, plans, or concepts to clarify the combined use. This is the gap we
are addressing.

The contributions of this work can be summarized as follows:

  1. 1.

We propose a novel concept to combine different technological trends,
including digital twinning, VR and mobile robots (the theoretical result), to
perform monitoring in DCs.

  2. 2.

The concept is implemented and validated in a novel case study in a real data
center to perform monitoring and inspection (the practical result). Such an
automated maintenance system in data centers has not been considered in the
literature so far (see more details in section 2).

The validation shows that the use of the DT-based methodology is feasible.
Thanks to the integration of VR, the proposed approach is more visible;
furthermore, it reduces costs and provides higher quality at high speed, as
proven in real operations. Therefore, it is very likely that potential
applications of our method will be found in different areas, such as other
asset-centered organizations like a power plant.

The paper is organized as follows. We begin with a description of the
motivation and reasons for choosing data centers as our case study in section
2. Then, our proposed concept and its development are described in sections 3
and 4, respectively, while the results of the application in a real data
center are presented in section 5. We will conclude this work with a short
summary and an outlook in section 6 with some suggestions for future research.

##  2 Background of maintenance in data centers

In this section, we describe the importance of performing maintenance in data
centers in subsection 2.1, common maintenance strategies in subsection 2.2,
and innovative approaches in subsection 2.3.

###  2.1 Data center

A data center (DC) containing a large number of servers and computers can
provide internet services for many companies in the world, such as Google and
Amazon. Servers and computers are stored in racks (e.g. 42U 1200mm deep racks
from Lenovo) in a DC. According to the categories mentioned in bieser2018role
, a DC can be an in-house DC (hardware and infrastructure are privately
owned), a co-locator DC (e.g., modular service packages are offered to users
with or without information technology (IT) applications), or a cloud DC
(services are offered over the Internet without users owning the IT
infrastructure). Each DC maintains important environmental factors such as
proper temperature and humidity. Due to the high demand for a wide range of
services (such as web search, e-commerce, video streaming, gaming, high-
performance computing, and data analytics), the number of DCs is growing
worldwide; in particular, the number of cloud DCs (such as Amazon EC2 and
Microsoft Azure) has grown rapidly recently. The COVID-19 pandemic has
significantly increased the use of online tools for applications such as video
conferencing, virtual business events, and remote education. This shift has
been enabled by cloud environments, especially public clouds. There are more
than 7 million DCs worldwide. According to Synergy Research Group, in addition
to the 600 operational hyperscale data centers worldwide, another 219 are in
the planning or construction phase (datacenter ). The largest technology
companies (Google, Facebook, Amazon, and Microsoft) have more than 50 DCs with
more than 1.3 million servers.

The primary mission of a DC is to provide reliable and continuous service.
Downtime in DCs should be avoided because it has a massive negative impact on
the business, such as loss of capacity, cost of defects, cost of delays, and
also the indirect costs caused by, for example, reputational damage. According
to research conducted by the Ponemon Institute in 2016, the average cost of DC
downtime was approximately $\$$8,000 per minute, and due to the average
reported incident length of 95 minutes in the survey, the average cost of a
single downtime event was $\$$740,357, a net increase of 38% compared to 2010
(ponemon ). According to Fortune, Facebook lost $\$$99.75 million in revenue
during the October 7, 2021 outage that lasted more than six hours. In
literature, limited researches concentrate on failure analysis in DCs, e.g.
gill2011understanding  for network failures, guenter2011managing  for hardware
failures and ram2014modeling  for various failures, such as failure of
redundant server, switch, router or server cooling. There are some methods
implemented in the literature to reduce failures. For example, high
temperatures and repeated on-off cycles of a server can increase hardware
failures. To avoid them, the workload distribution of servers is optimized by
considering thermal characteristics (wang2009towards ; guenter2011managing )
or with balanced energy consumption and quality of service (bayati2016managing
), or some new cooling systems are introduced (chen2013optimization ;
cho2015development ; li2016data ).

###  2.2 Traditional maintenance strategies in DCs

As mentioned in ignore , 30% to 40% of system timeouts due to infrastructure
hardware failures can be avoided through regular preventive maintenance. Note
that IT infrastructure includes servers, switches, routers, power supplies,
and cooling and monitoring systems. Preventive maintenance is defined as
“maintenance carried out at predetermined intervals or according to prescribed
criteria and intended to reduce the probability of failure or delegation of
the function of an item” (DIN EN 13306). Preventive maintenance can include
replacing backup batteries or generators, replacing servers and switches,
upgrading software, etc. In addition, preventive maintenance is either
performed according to an established schedule without prior condition surveys
(scheduled) or scheduled on demand or on the basis of performance and
parameter monitoring (condition-based). More about the definition and
categories of maintenance can be found in bieser2019assessing . The same
literature provides an overview of current maintenance concepts. The
importance of maintenance in DCs is highlighted in bieser2018assessing  from
an operational and economic perspective, and a preventive maintenance approach
is introduced. An overview of the main practical issues of DCs in terms of
maintenance management can be found in abadi2020data .

In this work, we focus on condition-based maintenance based on regular
monitoring. System monitoring is one of the research topics in maintenance
management. The traditional methods of monitoring a DC include using a
monitoring system (with sensors/cameras installed at fixed locations) and
manual inspection. Using the first method, it is expensive to install enough
sensors/cameras to monitor all assets in a DC. In addition, it is impossible
to confirm the situation on site when failures occur, and human personnel are
required to be on site. The second method requires human staff to check the
status of all assets, which is very time consuming. In addition, the
limitations of human vision and skill lead to most technical errors in
monitoring. Each infrastructure in a DC is monitored by the monitoring system;
when failures occur, the system detects them and alerts the human staff. The
traditional method of monitoring a DC requires human staff to be on site.
Wireless monitoring is discussed in kadir2015wireless ; a 3D gaming platform
is developed to enable real-time monitoring and display visual analysis of the
operating condition in a DC (hubbell2015big ).

As mentioned in abadi2020data , innovative approaches are needed to expand the
scope of traditional maintenance strategies in a DC. A recent overview in
levy2021emerging  mentions three main technological trends in DC management
automation that should receive more attention in the near future: 1)
intelligent monitoring and management systems using data science (e.g.,
predictive maintenance in decker2019big ); 2) simulation tools incorporating
artificial intelligence and digital twinning; and 3) robotics for process
automation. The second and third technological trends will be the focus of the
remainder of this work, as they enable the automated collection of vast
amounts of sensory information, which is seen as the basis for predictive
maintenance.

###  2.3 Innovative maintenance approaches in DCs

With the increasing size of DCs, monitoring in DCs is very challenging and
automation is a mandatory requirement of scale. Monitoring is a repetitive
task, and autonomous mobile robots can be used to perform round-the-clock
monitoring to increase process automation and productivity, and reduce human
error. In addition, the environment in DCs is not human friendly - it is
noisy, it can be cold and it can be hot. There is a growing demand for
hyperscale DCs that are more like warehouses, where robots need to navigate to
specific locations to perform tasks. It is also more cost-effective to use
mobile robots with built-in sensors to perform monitoring than to install many
sensors at fixed locations in DCs. Note that mobile robots are used to perform
monitoring and inspection in various industrial applications, such as offshore
platforms (gehring2021anymal ), power transmission lines (alhassan2020power ),
oil and gas industry (yu2019inspection ; roh2001activ), and deep underground
mines (zimroz2019should ). An overview of monitoring and inspection with
mobile robots can be found in kroll2008survey  and katrasnik2009survey .

Recent research in DC monitoring using mobile robots focuses on a robotic
platform for environmental monitoring (see rosa2014towards ; russo2016novel ;
terrissa2019robotics ). Environmental monitoring aims at measuring the
environmental qualities in DCs for diagnostic and control purposes. The
sensors are installed in mobile robots to monitor temperature, humidity and
air quality. The sensors can be, for example, a thermal camera and
temperature/humidity sensors (rosa2014towards ; russo2016novel ). In this
work, we focus not only on environmental monitoring, but also on asset
monitoring in DCs. Asset monitoring aims to check the quality of assets in DCs
around the clock, including servers, computers, switches, routers, network
equipment, and so on. For such repetitive and time-consuming tasks (due to the
large number of assets in DCs), mobile robots are required to perform
inspections. In this way, asset failures can be quickly detected (even
predicted) and it is possible to ensure continuous services from a DC.
However, such asset monitoring using mobile robots in DCs has not been
considered in the literature.

DCs are asset-centric organizations, and it is important to avoid significant
asset downtime (as mentioned above). A DT plays an important role in this, as
it is the virtual counterpart of an asset, allowing organizations to digitally
mirror and manage an asset throughout its lifecycle. To virtually represent
the life and behavior of the asset, a DT must incorporate all types of data
from multiple domains related to the asset and continuously provide
information about the condition of the asset, including its status and
maintenance needs. Citigroup is an example of how it integrated the digital
twin of the DC into its workflow to improve the efficiency of its New York
City DC. DT-designed solutions quickly reduced inlet temperatures by 27% and
generated over $290,000 in potential annual energy savings (see Citigroup ).
3D reconstruction methods can provide the DT component to drive a vision-
guided robotic inspection system in the manufacturing and remanufacturing
industries (khan2021inspection ). However, the DC digital twin has not yet
been addressed in the literature.

Asset monitoring is complex, and human intervention is still needed to deal
with robot errors, to recheck some assets, and to replace assets. Note that
robots that can automatically replace faulty disks are still being developed,
such as Alibaba’s Tianxun robot RobotsInDCs2 ; RobotsInDCs1 . Therefore, there
is a strong need for humans to interact with robots and their services in the
DT interface, e.g., to control robots to re-check a faulty disk. The DT lacks
human interaction capabilities.

In this work, we focus on the use of VR technology in a digital twin of a DC
to enable human interaction with robots to perform monitoring (i.e., automatic
inspection, faulty disk location, disk replacement), which is rarely addressed
in the literature. One existing paper is about an educational VR application
for teaching cybersecurity concepts in a DC (seo2019using ). Furthermore,
interaction with robots also allows remote control of robots in case of
failures.

##  3 The proposed concept

In this work, we propose a new concept to combine DT and VR to provide robotic
services (see Figure 1). Here, we use the DC environment and assets as well as
monitoring services to better understand the concept. They can be applied to
other use cases, such as robotics services in smart framing or other asset-
centric organizations, by changing the assets and the content of the services.
This concept extends the DT paradigm proposed by dietz2020digital  with
different layers, while VR is combined to provide visualization of DT objects
and remote control of the robots.

Four layers are involved in our concept: the hardware layer (physical layer),
which contains enterprise assets and a mobile robot (including a robot base,
an edge device, and various installed sensors, etc.); the service layer for
providing different types of robotics services in a robot management system
(RMS) based on the information collected by the robot, e. g. the data layer
for providing/collecting data (of any type, e.g. sensor-based, reports, video
data) to/from different layers; and the application layer (digital layer) for
representing the virtual counterparts to the assets and the robot in the
hardware layer as well as their displays.

Figure 1: The concept for the realization of the digital twin and virtual
reality to provide robotics services in a DC.

In the application layer, a virtual DC is modeled by the DT and the
information provided by the service layer is displayed by the VR. Figure 2
shows a real DC and our developed DT DC in the same scene.

Figure 2: Real DC vs Digital Twin DC (including an inspection robot).

Our DT is built from the following blocks, similar to dietz2020digital :

  * •

Data includes static and dynamic data. The static data is provided by the
assets from the hardware layer to model the DC, which is considered to be
static, while the dynamic data, including the sensor/camera-based inspection
data (e.g. temperature, humidity, server status) and robot information data
(e.g. robot status), is received from the RMS.

  * •

Semantic technologies are used to describe the relationships between data
elements to better understand their meaning.

  * •

3D simulation is applied and combined with data to represent a DC to study its
behavior.

  * •

Analysis, intelligence and other services are supported in the DT ranging from
asset monitoring to autonomous driving of robots. For example, historical data
can be analyzed to perform predictive maintenance.

  * •

Interfaces and access control are the mechanisms that connect the virtual and
real worlds. They allow data exchange and synchronization. The bi-directional
connection is enabled between the asset/robot and its twin, also between the
DT and VR. The development of the interface is described in section 4.

Sherman & Craig defined four key elements of the VR experience in VR2018book :
virtual world, immersion, sensory feedback, and interaction. VR is seen as one
of the tools with the most potential to enable human-robot collaboration. The
most commonly used VR headsets connected to a computer are Oculus Rift, HTC
Vive and Pico Neo. Our DT system includes several interactive VR elements. Our
VR is built from the following blocks.

Figure 3: The VR data center with virtual staff from the robot’s perspective.
Figure 4: The VR data center with a virtual inspection robot from the virtual
staff’s perspective.

  * •

DC visualization shows the virtual DC environment which is reconstructed in
the DT.

  * •

VR interactive includes teleoperation and visualizes the robot together with
the DC environment in 3D, information such as the state of the robot and
knowledge database.

  * •

Robot control sends a request for the robot control service from the RMS to
fulfill a given task.

  * •

Robot monitoring sends a request for the robot monitoring service from the
RMS, including a representation of the robot and its movements, information
about the robot’s state and the measurements from on-board sensors, images
from cameras, and the progress of the ongoing inspection tour.

  * •

Display includes the display of asset information, video stream, inspection
reports, alarms and visitor information.

  * •

Interfaces and access control have the same purpose as in the DT. The bi-
directional connection is enabled between VR and the RMS, and also between the
DT and VR. There is more about the development of this interface in Section 4.

  * •

Virtual staff is a realistic human character, which is developed by MetaHuman
Creator, a free cloud-based app for creating a fully rigged, photorealistic,
digital human (see MetaHuman ).

Unlike the usual VR interface for a single robot as in roldan2019multi , where
the interface is centered in the robot and its payload, and a first-person
view of the robot is adopted, we have different views in our VR interface,
including a first-person view of the robot in Figure 3 and a first-person view
of the virtual staff as shown in Figure 4. Using the images from the robot’s
camera in our virtual environment, we can identify an asset and display its
type, maintenance manual, etc. via the interface, display a real-time video
stream, identify the location of an alarm, or identify the person (visitor or
employee, etc.) using facial recognition (for security purposes) and display
their personal information. We can also display the inspection reports through
the VR interface. Our VR interface can easily support multi-robot systems by
selecting the camera of one of the robots and we can change the view from one
camera to another. As shown in both figures, a virtual interactable object is
attached to the front of each rack, which has the following two functions.
First, it provides users with inspection information about their rack. Second,
it can be used as an input device within VR, where the user can give commands
to the system by interacting with warning messages.

Figure 5: DT and VR system diagram (expanded and modified on Kuts2019dt ).

In Figure 5, we extend and modify the schematic representation of the DT and
VR system from Kuts2019dt  designed for industrial robot arms. Note that such
a system is widely used to program motions for an industrial robot arm.
Similarly, we use the DT controller as the core, and we import the computer-
aided design (CAD) model of the robot (from the robot manufacturer) into the
VR environment. The virtual robot receives position synchronization from the
DT controller and sends collision data. The real robot sends status data and
receives control commands through the local network. The following
modifications are made in our system. First, we connect the DT controller to
the RMS (instead of a robot controller) to enable services such as receiving
robot status and inspection data. Second, in addition to the virtual robot, we
have a more complex virtual environment that also includes a virtual DC and
virtual interactable objects. The virtual interactable object controller sends
interaction data to the DT controller and receives inspection data from it.
When the user sends commands, they are sent through VR to the DT controller.
The DT controller then sends commands to the RMS, and the RMS controls the
robot over the local network. After the real robot has sent the status and
inspection data to the RMS via the local network, the data is sent to the DT
controller. The actual data is displayed in VR, and the inspection data is
updated in the virtual tablet by the virtual interactable object controller.

##  4 Development of our concept

To implement the concept explained in the previous section, we designed a
development process as shown in Figure 6. Our VR interface is developed based
on the use of Unreal Engine and OpenXR. Unreal Engine is a 3D computer
graphics game engine developed by Epic Games. The authors in hubbell2015big
list several reasons why a game engine can be successfully applied to DC
infrastructure management, including the ability to convey interactive,
actionable data, platform independence, ease of use for users, and the use of
tools in the 3D environment, such as colors, shapes, morphing an object, and
in-game sounds. OpenXR is a royalty-free, open source standard that provides
high-performance access to XR platforms and devices (see OpenXR ). The OpenXR
plugin is a library of built-in classes and components to support VR.

In the first step of the development process in Figure 6, we collect the data
center modeling data, such as a blueprint of a DC and the enterprise assets.
We use Blender, which is a free and open source 3D animation suite, to create
the DC model, while the robot modeling data is used to create the robot model.
Then we export the model into the .fbx file format (a format used to exchange
3D geometry and animation data), which is used as the import chain of Unreal
Engine.

Figure 6: The development process for our DT and VR.

In the second step, we determine the settings in Unreal Engine, including the
settings for Unreal’s built-in VR support, device-specific toolkits (for
example: SteamVR plugin, Oculus integration toolkit, Pico Unreal XR SDK),
application toolkits, target platform, and XR SDK. In the third step, we place
the DT in the scene to visualize the DT, the robot, and interaction objects
such as a virtual tablet, as shown in Figure 2. In the fourth step, we set up
the user interface and video player in the Unreal motion graphics UI (the area
where all UI elements should be located), while in the fifth and final step,
we write the blueprint visual scripting scripts or C++, which includes the
following main functionalities REST API handler; database handler; RTSP (Real-
Time Streaming Protocol) client for receiving the video stream; event and
messaging; interaction (e.g., interaction with the virtual tablet, virtual
robot, etc.); and controls and input for controlling the robot. More technical
details of these functionalities can be found in Figure 7.

Figure 7: Overview of the system technical architecture.

As shown in Figure 7, the basic components and structures of our DT and VR
interaction system are an inspection robot, the RMS, and the DT and VR
environment.

The Robot Operating System (ROS) is the de facto standard for robotic software
development and provides a collection of packages and tools for developing
distributed robotic applications. The inspection robot developed using the ROS
algorithms and services has capabilities such as AI for sensing, path
planning, motion control, safety and human-robot interaction (HRI), wide field
sensing, vision, and autonomous navigation.

The robot and the RMS are connected via a Transmission Control Protocol (TCP)
connection over a local network using the ROS TCP connection. The RMS is
developed in Python. We use the web framework and the asynchronous networking
library Tornado as the back-end on Ubuntu and MySQL-based data storage, while
the front-end is developed in JavaScript using Vue.js (an open-source model-
view-view-model front-end JavaScript framework). To enable the RMS to
communicate with robots from different manufacturers, it is extended by other
communication interfaces, e.g. MQTT. The RMS has capabilities such as robot
control and mission editor, robot monitoring, real-time data streaming, data
center monitoring, and security services, which are implemented as RMS
algorithms and services.

The RMS provides REST API and RTSP services to our DT and VR clients. The RMS
and our DT and VR clients are connected via HTTP (Hypertext Transfer Protocol)
as a REST API server and client, respectively, while the RTSP ("real-time
streaming protocol") connection allows the RTSP server and client to be
connected over a local network. The DT and VR client runs on the OpenXR
backend, and its DT controller, as described in Figure 5, is used to connect
the RMS to VR and DT components such as the virtual robot. The DT and VR
client sends commands and receives data from the RMS via the RESP API client,
while the video stream data is received by the RTSP client.

In order to achieve all of the above, the following special plugins or APIs of
Unreal Engine 5 are required for development in the DT and VR environment.

  * •

The Multi Task 2 plugin is used to enable multi-threading in Blueprints Visual
Scripting System (MultiTask2 ).

  * •

The VLC plugin (VLC is a free and open source cross-platform multimedia player
and framework; see VlcMedia ) is selected, which is adapted and built for
Unreal Engine 5. It is used to handle and play the RTSP stream.

  * •

The VaRest plugin is used to handle the REST communication (VaRest ). The DT
controller receives the robot’s position information, its motion information,
its angle, the position for the vertical lifting/lowering module of the HD and
thermal camera on the robot, and a number of related robot information from
the RMS via the REST API every 0.5 seconds. This information needs to be
synchronized with the DT of the virtual robot (see figure 2) and then
visualized, which requires some smoothing on 3D graphics.

  * •

The DataRegistries plugin is used to store, combine, read and manage data from
different sources for the implementation of the database handler (see
DataRegistries ).

  * •

Mathematical APIs on the Unreal Engine are used to perform some interpolation
operations to smoothly realize the position of the virtual robot (see
MathInterpolation ).

##  5 Validation of our concept in a real DC

In this section, we first describe the real DC environment in Subsection 5.1,
the robotics services in Subsection 5.2, and the data processing from various
sources in Subsection 5.3. We then describe the DC inspection workflow in
Subsection 5.4 and the results of applying robotics services to monitor the
real DC in Subsection 5.5.

###  5.1 DC environment

As a case study, we implement our concept in a data center with an area of
522.5 m2, including 140 racks. It includes the following assets in the
physical layer of Figure 1:

  * •

Computing resources including servers, computers and networking equipment
(servers and computers are located in racks).

  * •

Non-computing resources including cooling and power devices.

An inspection robot (in the physical layer), as illustrated in Figure 8, is
used in the experiment. The robot includes the following two types of
components:

Figure 8: An inspection robot in a DC environment.

  * •

Components to inspect

    * –

Vertical lifting & lowering module to move the HD and thermal camera for
changing inspection targets at different heights of the same location (max
inspection height is 2.4 meters; see e.g. the robot in Figure 2 in the lifting
module).

    * –

Inspection sensors/cameras including HD camera and thermal imaging camera used
to collect equipment information and prepare for asset monitoring. The
location of the server is predefined on the map based on the server number, so
the camera can quickly locate the server number during the inspection;

    * –

Environmental sensors (not visible in the figure), including a temperature and
humidity sensor, a noise detector, and an air quality sensor (PM 2.5 sensor),
which are required to collect environmental information for DC environmental
monitoring.

    * –

Touch screen, spherical camera, microphone and speaker for Human-Robot
Interaction (HRI) and video intercom function for remote call.

    * –

Edge device (not visible in the figure) for AI inference and data analysis for
inspection (visual inspection tasks, acoustic and thermal inspection). It is
able to detect the defect information from the light signals coming from the
servers and extract its information with the help of a machine learning
algorithm.

  * •

Autonomous driving components

    * –

Built-in computer (not visible in the figure) as the navigation PC, which can
perform real-time and high-performance computation for sensing and
localization, mapping, path planning and following, and mission planning.

    * –

Navigation sensors including Lidar and depth cameras (to determine the robot’s
position relative to the known environment, simultaneous localization and
mapping (SLAM) by fusion of Lidar and depth cameras) and ultrasonic sensors
(to detect the motion of objects and measure the distance to them), which are
needed to enable the robot to navigate and avoid obstacles and collisions in a
DC. For optimal obstacle avoidance, the depth camera can be installed; by
using the depth camera, the robot can "see" and understand its surroundings.
The localization process determines the robot’s current position on the map
based on input from the motor encoders, the inertial measurement unit (IMU)
and the lidar.

    * –

Differential drive base to ensure the differential drive of the mobile robot,
i.e. the movement of the robot is based on two separately driven wheels placed
on either side of the robot body (the maximum speed of the robot is 1.5 m/s
and the acceleration is limited to 0.3 m/s2).

    * –

Charging contacts to contact the charging station for battery charging.

    * –

The battery (not visible in the figure) on this inspection robot has a
capacity of 1.56 kWh (30.4 Ah at 51.2 V). The battery type is lithium-ion (Li-
ion). The active operating time at full load is approximately 6 hours. It
takes about 1 hour to fully charge (from 40% to 100%).

    * –

Others: The robot has an emergency stop button on each side and a start
button; the wireless network antenna is located inside the robot; LED lights
are used to display the moving or operating status of the robot and some
execution status items such as charging and internal errors in the robot. A
remote module (network relay module) sends a signal via TCP/IP to the door
opener controller to ensure the opening and closing of the DC door.

###  5.2 Robotics services in the DC

In the service layer of Figure 1, the RMS receives the information collected
by the robot and provides the following services via an a user-friendly UI
(user interface) as shown in Figure 9, which are accessible from the data and
application layers.

Figure 9: The UI of the RMS for mission planning. Figure 10: The data flow
process of the DC inspection with robot.  Figure 11: Equipment and
environmental inspection.

The following robotics services provided in the RMS are developed in the DC:

  * •

Robot control and mission editor, which specifies the robot’s tasks,
including, for example, recharging and executing an inspection plan based on a
mission plan. In Figure 9, the path and inspection points (blue points with
numbers) of the inspection mission are defined by the user. The user can
select the mission and its start time. The RMS is able to automatically send
the robot to charging stations when it starts to reach low power.

  * •

Robot monitoring including the state information of the robot and the
information provided by the installed sensors and cameras, etc. In case of
failure or undefined behavior of the robot, the monitoring will display the
warning information and a remote human operator can be contacted.

  * •

Real-time data streaming is processed in the edge device using the real-time
data recorded by the HD camera and the thermal imaging camera of the robot.

  * •

DC monitoring and inspection data flow process with the robot (in Figure 10)
including:

    * –

Transferring the sensor data (from various sensors) to the edge device to
perform AI inference and process the images and data, such as the thermal
image of a server in a rack and the DC temperature distribution and status of
a server (via external status indicator LEDs on the front panel of the
server);

    * –

Send images and data from the robot to the RMS to generate alarms and reports.

  * •

Security service preventing unauthorized access to DCs through AI facial
recognition processed in the robot’s edge device. In this case, the spherical
camera on the top of the robot is used.

Note that the identification of each rack and the servers in the rack is
achieved by their predefined location in the RMS. We have proposed an
equipment detection model based on the Darknet YOLO framework, similar to the
work in yolo , which uses a deep learning-based solution for real-time
inspection. Darknet is an open source neural network framework written in C
and CUDA (darknet ). The robot’s equipment and environmental inspections are
shown in Figure 11.

###  5.3 Data sources

The data layer can access data from different sources (of any type, e.g.
sensor-based, reports, video data) in the hardware and service layers. A MySQL
database is created in this layer and made available for use in the
application layer. Multiple data representations in the database support the
connection of the video stream, the information stream, the control stream and
the inspection robot system. The data sources we use include:

  * •

Video data recorded by the robot’s HD camera and thermal camera (using the
H.264 video compression standard in MP4 (MPEG-4 Part 14) format).

  * •

Inspection data processed in the edge device; the visual inspection, thermal
and acoustic inspection, and other inspection data are recorded (JSON format).

  * •

Sensor data including the environmental sensor data from the robot (JSON or
XML format),

  * •

Report data including inspection reports generated by the RMS (PDF and Excel
format)

  * •

Robot log data recorded while the robot is performing a task (stored in the
database in XML format).

  * •

Human-robot interaction data including human-robot interaction via tablet,
camera and microphone (JSON format)

  * •

VR interaction data including VR and DT system interaction data (JSON format)

In the DT and VR system, the Unreal Engine plugins – Json Blueprint
(JsonBlueprint ) and EasyXMLParser (EasyXMLParser ) – are used to create and
parse the JSON string and get values from XML.

###  5.4 DC inspection workflow

The Pico Neo 3 Pro VR headset with installed DT and VR software, the RMS
software on a computer, and an inspection robot are used in the experiments.
The schematic workflow of the integrated DT and VR in the robotic inspection
system is illustrated by the flow chart shown in Figure 12.

Figure 12: Flow chart presenting overview of our DT and VR in robotic
inspection system in DC.

There are three phases: setup, installation, and inspection. In the setup
phase, the DC and the robot are first set up in 3D using DT and VR
technologies (as described in section 4). This phase needs to be performed
only once for each data center and inspection robot.

In the installation phase, the robot is first deployed. Then the robot is used
to map the DC by fusion of lidar and depth cameras. The user can use the RMS
to set the start position of the robot to approximately place the robot
correctly on the map. The user plans and manages inspection missions to tell
the robot where to go and what to do. The missions are programmable by the
user through the RMS user interface. Some functional tests of the robot’s
localization and navigation system are performed. The visit points of the
robot and the visit path in the DC can be set in the RMS (in Figure 9). The
RMS synchronizes data with the robot and retrieves all site data.

Once the installation phase is complete, inspections can be performed multiple
times in the following manner, either on-site or remotely. The robot performs
its inspection mission (defined in the installation step), reading inspection
objects (by checking external status indicator LEDs on the front panel of the
server), scanning the environment for anomalies, and recording results. An
algorithm based on machine vision technology is developed to detect status
indicators in low-light environments. In addition, the robot provides real-
time information on the status of each mission to the VR software, including
the temperature distribution data of all assets captured by the thermal
imaging camera (and the resulting 3D heat map) and the data captured by the HD
camera (see Figure 10). We record the entire inspection process in the VR
software. The inspection can be performed by the robot alone (automatic mode),
or we can use the VR software to monitor and interact with the robot in real
time during the inspection mission (manual control mode). In addition, post-
analysis can be performed, such as displaying an inspection result report,
replaying and analyzing the recorded inspection process, or analyzing the
recorded environmental data.

We test the software from the perspective of the virtual worker and the robot.
From the virtual staff perspective, we can control the virtual staff moving in
the DC and interact with the virtual tablet on the front of each rack, which
shows the warning or error information of the servers in the rack. By clicking
on the rack, the server maintenance and specification information is displayed
based on the knowledge graph database (in Figure 13). The real-time video
stream, robot and data center information, and robot control are displayed by
the software. From the robot perspective, we can get the warning information
and set the inspection mode such as automatic and manual control. In the
manual control mode, in addition to the inspection mission, we can, for
example, control the lift of the robot and make some adjustments for the
camera panning.

Figure 13: The knowledge graph database in VR with first-person virtual staff
view.

###  5.5 Results

We conducted a one-month test in the DC, performing daily inspections and
completing a total of 30 inspection missions. As shown in Figure 9, this is a
typical inspection mission with 114 inspection points and an average
inspection time of about 70 minutes.

By manually reviewing the recorded video captured by the robot, the accuracy
rate of the automatic inspection by the robot can reach 99% (by checking the
external status indicator LEDs on the front panel of the server). The
information is continuously updated in the VR and DT system. This greatly
reduces the workload of DC personnel.

The system installation work needs to be done only once to create the map and
DT environment, including setting up the automatic charging station and doors.
For inspection, we only need to start the software, start the RMS and robot,
and set the inspection mission plan. The DC staff can either start the DT and
VR for real-time inspection, or do the monitoring after the automatic
inspection by the robot and check the DT and VR system.

Figure 14 shows the captured inspection results and identified anomalies
report of a typical inspection mission. Such a report consists of three parts:
environmental inspection, equipment inspection, and detailed alarm
information. The captured images of alarms are also included in the report.
Inspection reports are saved directly in PDF and Excel formats.

Figure 14: The inspection report.

##  6 Conclusions

In this paper, we introduce a novel concept to realize a real-time immersive
automated system that combines digital twin with virtual reality, since
automated systems are widely used in practice. In addition, the digital twin
models not only enable testing before they occur in the real systems, but also
enable predictive analysis based on the digital twin data. In addition, the
combination of the digital twin with virtual reality helps human personnel to
interact with a robot remotely in both the physical and digital worlds. This
concept will be implemented and validated to perform monitoring and inspection
in a real data center to reduce downtime and ensure reliable and continuous
data center services. We chose the data center application because of the
rapidly increasing demand for data center services, especially during the
COVID-19 pandemic. Also, our concept is considered an innovative approach in
data centers that has not been considered in the literature. The validation
shows that the use of the combined methodology is feasible. The system we
developed has several advantages. First, the use of mobile inspection robots
enables the automation of the inspection (autonomous driving and data
collection via sensors/cameras), which improves productivity while reducing
human workload. Thermal image analysis and acoustic analysis are automatically
generated based on the data collected by the robot. Second, reliable
information can be obtained through advanced sensor technology and AI-based
algorithms that avoid human error and ensure the accuracy of identification.
In our one-month test in a real DC, the accuracy rate can reach 99%. Third,
the human staff can work remotely using digital twin and virtual reality to
control the robot to carry out real-time inspection. By using digital twin and
virtual reality, the effect of human-robot collaboration can be achieved.

In the future, our concept can be applied to other automated applications,
such as smart framing, power plants, and industrial processing areas (each
with a different type of robot). Data analysis in digital twin data centers
for fault prediction, predictive maintenance, and energy efficiency will be
research topics in the near future. Validation in the near future will include
user feedback, such as the usefulness and ease of interaction with the virtual
reality interface. Our digital twin and virtual reality system in data centers
is a promising area with high potential for enhancing the training of data
center professionals. Therefore, offering training programs using our robotic
inspection system may be an issue in the future.

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