ORIGINAL PAPER
A methodology to develop collaborative robotic cyber physical
systems for production environments
Azfar Khalid
1,3
•
Pierre Kirisci
1
•
Zied Ghrairi
2
•
Klaus-Dieter Thoben
1,2
•
Ju
¨
rgen Pannek
1,2
Received: 25 November 2015 / Accepted: 25 October 2016 / Published online: 8 November 2016
Ó The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract The paper identifies the need for human robot
collaboration for conventional light weight and heavy
payload robots in future manufacturing environment. An
overview of state of the art for thes e types of robots shows
that there exists no solution for human robot collaboration.
Here, we consider cyber physical systems, which are based
on human worker participation as an integrated role in
addition to its basic components. First, the paper identifies
the collaborative schemes and a formal grading system is
formulated based on four performance indicators. A
detailed sensor catalog is established for one of the col-
laboration schemes, and performance indices are computed
with various sensors. This study reveals an assessment of
best and worst possible ranges of performance indices that
are useful in the categorization of collaboration levels. To
illustrate a possible solution, a hypothetical industrial
scenario is discussed in a production environment. Gener-
alizing this approach, a design methodology is developed
for such human robot collaborative environments for var-
ious industrial scenarios to enable solution implementation.
Keywords Cyber physical system Human robot
collaboration Collaborative robotics
1 Introduction
The manufacturing horizon for Industry 4.0 [
1] comprises a
paradigm shift from the automated manufacturing toward
an intel ligent manufacturing concept. The exclusive feature
in Industry 4.0 is to fulfill the real-time custom er demand
of variations in products in a very small lot size. This will
enable a manufacturing system to meet individual customer
requirement without wasting time for setup and for re-
configuration of an assembly line. The intelligent manu-
facturing implementation may take place though the con-
cept of internet of things (IoT) [2], in which each
participating component has a specific IP address. Due to
the availability of big data in IoT, the manufacturing sys-
tem characteristics can be predicted precisely like predic-
tive maintenance, robustness in product design and
adaptive logistics. In this context, the smart manufacturing
setup or a smart factory [
3, 4] and logistics system have to
fulfill the mass customization [
5] demand in a flexible
manner.
For a smart robotic factory to work in the context of
Industry 4.0, high productivity and flexibility is the demand
of the future. To cope with this issue, robots may take most
of the workshare in future manufacturing, yet the human
worker has to stay in the work area either in supervision
role or for the jobs for which the robots cannot be trained.
The constant human presence in or near the work area of
intelligent robot leads to a shift regarding safety. The
conventional approach is to expose human workers up to a
limited extent to the robot and with appropriate safety
control that leads to full stoppage (safe hold) of a machine
in case of worker violation of the robot workspace. This
causes interruptions and resetting procedures to be acti-
vated which reduces productivity. The futuristic approach
is to implement robotic appl ications where robot and
This article is part of a focus collection on ‘‘Dynamics in Logistics:
Digital Technologies and Related Management Methods’’.
& Azfar Khalid
kad@biba.uni-bremen.de; azfar.khalid@cust.edu.pk
1
University of Bremen, Bibliothekstraße 1, 28359 Bremen,
Germany
2
BIBA-Bremer Institut fu
¨
r Produktion und Logistik GmbH
(BIBA), Hochschulring 20, 28359 Bremen, Germany
3
Department of Mechanical Engineering, Capital University
of Science & Technology (CUST), Islamabad, Pakistan
123
Logist. Res. (2016) 9:23
DOI 10.1007/s12159-016-0151-x
human workers can coexist and collaborate safely. In this
setting, the robots share the same workspace with human
counterparts and perform activities like raw material han-
dling, assembly and industrial goods transfer.
Due to the presence o f more than one million conven-
tional (non-collaborative) working robots in the industry
[
6], converting the present day conventional robots to
collaborative ones presents a lot of revenue potential.
These conventional robots cannot be replaced with new
collaborative robots (see Table
1) in manufacturing areas
because of the huge financial cost involved. One approach
to convert these conventional robots into collaborative ones
is by making their environment intelligent, e.g., by putting
sensors around the robot working area in addition to the
capturing of human worker motion. This way, multiple
conventional robots will be able to collaborate with
humans. This will be an advantage for the manufacturers as
capital investment on newly developed collaborative robots
may not be required. To establish such a collaborative
environment, a cyber physical system (CPS) needs to be
established which takes care of all the necessary require-
ments of communication, safety, secur ity, sensors and
electronics. This will also allow even very large payload
robots to carry out the tasks in a collaborative manner as is
the case in the small to medium payload robots shown in
Table
1. In one such attempt in MIT [7], the human motion
capturing sensors are used with a non-collaborative robot.
The virtual component resembling the actual scenario of
man and robot is used to calculate the distance between the
robot and the human. Based on the real-time distance
calculation, the robot controller is given the task by an
external module to systematically reduce the speed. This
way, a generalized solution is sought to make a conven -
tional robot intelligent.
2 State of the art in collaborative robotics
The state of the art development in collaborative robot ics
has roots in the technologies arriving from the humanoid
robotics, artificial intelligence and exoskeletons, which
were developed over the last two decades. The basic
objective of such robotic humanoids is to work in house-
hold and medical applications to attend the needs of dis-
abled and old people. In the industrial domain, there is only
a recent trend for the development of intelligent collabo-
rative robots. Table
1 shows many examples for such
collaborative robots that can work alongside humans
without creating hazardous situations. So far, the collabo-
rative robotics is developing fast in industry and it is
estimated that the collaborative robotics sector will grow to
US$1 billion by 2020 [
6, 8]. This growth is driven by small
to medium manufacturing, electronic manufacturing and
allied services provider companies. For industries looking
for such agile manufacturing technologies, robot manu-
facturers develop collaborative robot designs which are
suited for small- to medium-size product handling and
other operations.
In Table
1, multiple examples show dexterous robots
comprising of single or dual arms that have multiple
degrees of freedom (DOF). In most of the cases, the tool
end-effector repeatability shows the capabilities of mod-
ern collaborative robots to handle intricate tasks. All of
these robots can work and collide gently with humans on
the factory floor as the joints are developed with internal
force sensors. The arms and heads are equipped with
high-resolution cameras, even 3D cameras for tracking. In
some cases [
15–17], visual markers are used for fast
recognition and tracking, on every tool which are needed
to the robot to complete the job. All the robots have
programmable compliance, such that they can be trained
for the new job on the shop floor. Yet, the maximum
payload capacity varies from 0.5 to 14 kg, i.e., small- to
medium-sized payload. Collision detection, instant hold
upon collision and speed reduction upon violation of
workspace are the common implemented technology
features. It seems that there is a paradigm shift in the role
of robots in industry and services from conventional
unintelligent robots to collaborative ones. Also, these
recent developments range from small- to medium-scale
payload applications in human–robot collaboration
(HRC), paving the way for heavy robots to become col-
laborative as a next step in industrial collaborative
robotics. A very recent example is of FANUC’s CR-35iA
[
18] capable of carrying 35 kg payload with category 3,
performance level (PL) (d) safety certification, according
to ISO 10218-1:2011.
The paper has two basic objectives: The first aim is to
identify the collaborative schemes and formulate a formal
grading system; secondly, to define a CPS for huma n–robot
collaboration in industrial scenarios and develop a
methodology that can search for appropriate solutions in a
given industrial scenario down to sensor level. The latter
allows us to convert conventional heavy payload robots to
intelligent ones for any industrial setup. Further detailed
considerations for an equipped external environment for
such robots are derived from pre-defined safe CPS
according to the scenario requirement and collaboration
level sought. The approach is initiated by studying imple-
mented robot safety schemes and then evolving effective
collaboration schemes. Once the collaborative schemes are
sorted, some key indicators are introduced for formal cat-
egorization of industrial collaborative scenarios with
examples of few selected sensors. A hypothetical collab-
orative example is presented to identify the sensor level
requirements for a given industrial scenario. The paper is
23 Page 2 of 15 Logist. Res. (2016) 9:23
123
summarized with a design methodo logy for the develop-
ment of such CPS in the context of variation in industrial
scenarios.
3 CPS in human robot collaboration
The proposed approach is to exhibit safe intermediate HRC
without passive safety mechanisms (e.g., fencing). In order
to realize this, extra safety and protection measures need to
be implemented for a collaborative robotic CPS (CRCPS).
These safety and security (protection) requirements are
based on the level of interaction between humans and
robots on the shop floor to increase productivity. Security is
moreover closely related to safety as both these system
level properties have to be considered concurrently.
Security essentially protects the systems from humans as
attackers and the safety physically protects humans from
the systems (e.g., avoiding collisions). In fact, the approach
in the design of CRCPS is to merge the safety and security
concerns just like designing industrial facility, control and
risk assessment that consider both aspects [
19]. However,
in this paper, only the safety aspects are considered for
CRCPS development because security can be studied in
this specific case only once a safe HRC system is ensured.
Security is left as the future direction of current research on
CRCPS development to secure a ‘safe HRC system’ from
the cyber-attacks.
Table 1 State of the art collaborative robots
Robot Application area Specifications Main sensors Capabilities
ABB Switzerland,
Yumi—IRB 14000
[
9]
Mobile phone, electronics and
small parts assembly lines
Payload—0.5 kg
Reach—559 mm
Repeatability—0.02 mm
Foot print size—
399 mm 9 497 mm
Weight—38 kg
Velocity—1500 mm/s
Acceleration—11 m/s
2
Camera-based object
tracking
Collision detection
through force sensor
in joint
Dual arm body
Pause motion upon
collision
Action resumption only
by human through
remote control
Collision free path for
each arm
Rethink Robotics,
Boston, USA,
Sawyer [
10, 11]
Machine tending, circuit board
testing, material handling,
packaging, kitting etc.
Payload—4 kg
Reach—1260 mm
Repeatability—±0.1 mm
Weight—19 kg
Camera in wrist
Wide view camera in
head
High-resolution force
sensors embedded at
each joint
Force-limited compliant
arm
Seven DOF single arm
robot
Touch screen on the main
column for instructions
Context-based robot
learning
Universal Robots,
Denmark, U10
robot [
12]
Packaging, palletizing, assembly
and pick and place etc.
Payload—10 kg
Reach—1300 mm
Weight—28.9 kg
Velocity—1000 mm/s
Repeatability—±0.1 mm
Foot print size—Ø190 mm
Force sensors
embedded in joints
Speed reduction is
directly programmed
Six DOF in single arm
Collision detection
Robot stops upon
collision
Speed reduction to 20%
on workspace violation
NASA, USA,
Robonaut 2 [
13]
International Space Station, space
robotics
Payload—9 kg
Reach—2438 mm
Weight—150 kg
Velocity—2100 mm/s
Finger grasping force—
2.3 kg
Stereo vision camera
Infrared camera
High-resolution
auxiliary cameras
Miniaturized six-axis
load cells
Force sensing in joints
Dual arms with complete
hands and fingers
Each arm has seven DOF
Each finger has three
DOF
Elastic joints
KUKA, Germany,
LBR iiwa 14 R820
[
14]
Machine tending, palletizing,
handling, fastening, measuring
Payload—14 kg
Reach—820 mm
Weight—30 kg
Repeatability—±0.15 mm
Torque sensors in all
axis
Force sensors in joints
Contact detection
capability
Reduction in velocity and
force upon collision
Single arm robot with
seven axis
Logist. Res. (2016) 9:23 Page 3 of 15 23
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A CPS is a smart system in which the computational and
physical systems are integrated to control and sense the
changing state of real-world variables [
20]. The success of
such CPS relies on the sensor network and communication
technologies that are reliable, safe and secure. In CPS, all
the functional components are in modules and intercon-
nected (wirelessly) in the production line or in the smart
factory. Even raw materials and machines are connected to
the network cooperating with human workers through
human–machine interaction (HMI) systems. Hence, the
CPS platform evolves its architecture to engineer across the
digital–physical divide and removing the borders among
the key technologies. In particular, the CPS for manufac-
turing and production [
21–29] may consist of electronics,
computing, communications, sensing, actuation or robot,
embedded systems and sensor networ ks. The CPS in
manufacturing needs other resources like flexibility of the
manufacturing system, the manufacturing scenario and the
adaptability of changing assembly tasks [
30], in addition to
HMI technologies and other typical CPS modules. For the
application in HRC, the deployment of a full scale CPS
accounts for the human worker as an inherent part of the
system. To state the CRCPS definition, the three compo-
nents are clearly evident in the model with detailed adaptor
modules (see Fig.
1). The CRCPS structure is inspired by
anthropocentric CPS (ACPS) [
16, 29, 31], mainly due to
the cohesion of the human as an inherent module.
The human component (HC), the physical component
(PC) and the computational component (CC) represent the
three main integrated entities. The interaction among the
three entities depends upon the advent of the enabling
adaptor technologies. The HC is well connected through
different adaptor technologies, e.g., accurate human posi-
tion tracking technology is essential adaptor in the CRCPS.
The CRCPS is a highly automated system as it removes the
boundaries between the composite elements, thus prefer-
ring their operational interactions. There are various HMI
technologies based on human senses of vision, acoustics
and haptics. The proposed CRCPS can utilize vision sys-
tem for detection, tracking and gesture recognition of
human workers. The robots can also be commanded using
acoustic signals from humans (e.g., voice control). Addi-
tionally, a variety of sensors and actuators can provide the
interaction between HC, CC and PC. There are standard
interactions shown between the components which have to
contribute with a role. Adaptor technologies are scenario
dependent and can be seen as plug and play devices. There
are other optional scenario-dependent interactions between
the standard components and adaptors in CRCPS.
The CRCPS is an extension of the CPS and for that
reason must show compliance to the system level proper-
ties of a CPS. For this, CRCPS must exhibit properties like
integrality, sociability, locality and irreversibility. More-
over, it must be adaptive, autonomous and highly auto-
mated [
32]. Integrality for CRCPS means that its functional
components are well integrated to perform self-organizing
tasks like learning and adaptation. The ability of CPS to
interact with other CPS through different communication
Fig. 1 Structure of CRCPS: detailed components, modules, adaptor technology modules and interconnected links
23 Page 4 of 15 Logist. Res. (2016) 9:23
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technologies defines the sociability. It will encompass not
only devices but also integrates humans as well. As an
example, if the two CRCPS are functioning in a close
physical distance, then the worker belonging to a CRCPS
must be able to interact safely with the robot that belongs
to the other CRCPS. Locality introduces the computational,
human and physical capabilities of a CPS, as bounded by
spatial properties of the environment. Irreversibility of the
CPS makes it self-referential in timescale and state-space.
The adaptive characteristic makes the system self-orga-
nized and evol ving. The autonomy [
16] refers to the roles
of functional components and the CPS itself as capable to
make independent decisions.
4 Collaboration classification
For CRCPS industrial environment, a smooth overlapping
of workspace zones of robots and humans is considered in
which both can interact. The formal grading of the human–
robot collaboration involves the level of interaction
between the two entities. The level of interaction can be
formalized based on the distance between the two entities,
workspace share level and the complexity of collaborative
tasks which both are performing mutually. Many human
avoidance schemes based on human activity prediction or
human and robot position estimation at the same time
[
33–35], risk prediction cont rol [36] and augmented reality
[
37] are considered to be implementable in an interactive
environment. There are also fatalities reported [
38]in
countries where usage of robots is intensive despite putting
all the safety and protection protocols. For example, in
Germany, such accidents range from 3 to 15 annually from
2005 to 2012. Note that this rate relates to accidents
without any collaboration between humans and robots.
There is also an issue of mental strain on humans in
addition to the physical interaction of robot and huma n. It
is discussed by Arai et al. [
39] that by restricting the
moving area and moving speed of robots, the mental strain
of a human operator remains low. Also, the prior accurate
information of robot motion is essential to decrease the
strain on a human operator. In this context, there is general
need to classify the collabo ration level and specific to
heavy payloads, it is obligatory to reduce the level of risk
in HRC.
To formally grade the HRC, the safety approaches in
practice must be known first. All the examples shown in
Table
1 follow at least one safety approach during human
robot interaction. Safety schemes based on position pre-
diction and building intelligent environment [
40] around
robots are summarized. The intelligent environment means
to equip the robot environment with appropriate monitoring
sensors to make it aware of situation, human, safety zone
and distance. However, the four basic principles of safety
protection of working with robots are described in [
41, 42].
Here, these approaches are outlined briefly.
A common approach using small size robots is to pro-
vide guidance manually or reduce the robot speed as per
requirement. This manual approach is open loop, without
sensing, has high HRC level, is restricted to small size
robots and depends on the defined risk assessment. The
basic safety approach can be termed as ‘complete isola-
tion’. In this approach, a specified work zone is covered
with sensors like laser scanner or proximity sensor. In this
case, the robots must stop at the human access to the work
area. These systems are sensor dependent, closed loop and
have almost no HRC level attainment (see Fig.
2 for col-
laboration schemes).
The third approach is the speed and separation moni-
toring through vision-based systems or other possible
techniques. Speed reduction schemes of robot can be
applied with a possible stop or speed reduction in case of
worker enters the dangerous zone. This safety concept uses
multiple integrated senso rs and an effective sensor fusion
technique to develop a fast, reliable real-time monitoring
solution for HRC. High HRC level attainment is possible
but poses challenges to the risk assessment in case of a
failure of a monitoring function. The speed monitoring can
be integrated with separation monitoring, in which human
avoidance algorithms are used in a dynamic human track-
ing context. A small active area around the human position
is marked and continuously updated for the human motion
in the robot work zone, forcing the robot to actively avoid
such a space. The last concept is the force monitoring
through the use of force sensors. This system will also
work with the help of a vision field which will guide the
robot in case of a human presence. The robot speed and
acceleration reduction will take place according to the level
of force allowed to hit a specific part of the worker’s body.
This scheme demands integration of force sensors in
addition to the sensor technology required for basic area
monitoring. The scheme provides highest level of HRC
attainment but poses a challenge to the risk assessment in
case of failure of any monitoring function.
By looking at different collaboration techniques, it is
possible to categorize these by several parameters. Figure
3
shows the collaboration level from low to high. There are
four equally weighted key performance indicators (KPIs)
selected to contribute in the overall HRC grading scheme.
These indices are PL, safety distance (SD), risk (R) and the
reaction time (RT). PL is taken as the ‘mean time to dan-
gerous failure’ (MTTF
d
) and defined in the EN ISO 13849-
1 based on the average number of cycles per year until 10%
of the components have a dangerous failure.
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