October 17
6
C
oVEr
s
torY
Computer-on-Modules for
Robotics & Industry 4.0 Automation
By Knud Hartung,
ADLINK
Industry 4.0 and the availability of
technologies for collaborative robotics
continuously increase the intelligence
requirements in automation and ro-
botics. Computer-on-Modules enable
system engineers to adapt the compu-
ting cores to these evergrowing needs
most efficiently by offering flexible
scalability off-the-shelf.
Adlink_Coverbild_Sep17.indd 1
20.09.2017 14:45:04
Collaboration is a major trend in auto-
mation today: Industry 4.0 systems require
the controls of all the various machines and
robots to collaborate with each other. On top
of this demand for fully meshed control logic
there is also a transition happening where col-
laboration is not only based on the exchange
of digital information in real-time, but also
on artificial intelligence and situational
awareness empowered by deep learning tech-
nologies and powerful smart environmental
sensors such as intelligent cameras.
With all these new elements of collaboration,
vendors of traditional robotics and machine
controls are facing significant changes; and
those changes are happening fast. The collab-
orative robots market is forecast to grow at a
high CAGR of 56.94% between 2017 and 2023
and is expected to be worth USD 4.28 Billion
by 2023. This steep growth is attributed to
high ROI rates and low prices, making col-
laborative robots more attractive for SMEs,
as well as increasing industry investment in
automation to support the Industry 4.0 evo-
lution.
Engineers who want to be part of this inno-
vation wave are facing manifold challenges.
One major engineering task is the adoption of
Industrial Internet technologies to enable the
collaboration between the different systems.
Here, the engineer task is to enable their sys-
tems to communicate in real-time with other
systems; and with communication demands
increasing as more and more controls need
to coordinate with each other, bandwidth
demands are now rising from traditional 100
Mbit or 1 Gbit Ethernet performance to 10
GbE offered by new fog servers. Those servers
fulfill major higher-level analytics, decision,
communication, and control tasks in Industry
4.0 environments. Protocol implementations
for real-time communication such as a decen-
tralized data distribution service (DDS) need
to be managed here as well.
On top of this Industry 4.0 interaction
between the machine and robot controls, the
intelligence of each device needs to be ramped
up to enable real collaborative devices. Arti-
ficial Intelligence (AI) technology is one of
the drivers of the Industry 4.0 trend that is
expected to grow at the highest rates. AI means
dealing with simulation and implementation
of human intelligence on a computer. For this
intelligence, self-learning algorithms need to
be implemented alongside all the support-
ing sensor technologies that deliver the rele-
vant situational information that needs to be
analyzed for making decisions. The critical
challenge for manufacturers is turning legacy
machines and robotic arms that are tradition-
ally programmed to execute 100% predefined
movements into such intelligent machines
and robots. Drastically increased computing
performance is required to support all the
computing, measurement, motion control
and machine vision capabilities that will ulti-
mately enable customization of products and
flexible mass production on the factory floor
through collaborative intelligence. And look-
ing ahead, this computing performance needs
to be highly scalable to be able to fulfill future
demands.
There is clearly a massive amount of work
involved in implementing all these function-
alities – not to speak of the additional IoT
gateway requirements for OEMs to improve
field deployment, maintenance services and
on the fly deployment of new machine and
robotic functionalities. So how can engineers
fulfill all these new tasks under the high pres-
sures from market dynamics where first to
market is a major determining factor for gain-
ing market share?
One lever is to utilize existing ecosystems
and standards to streamline the engineer-
ing process by using off-the-shelf available
frameworks and open source software such
as real-time Linux or hypervisor technolo-
gies so that engineers can concentrate on the
application development. Another lever lies in
changing the way of designing the dedicated
