Applying the PROSA Reference Architecture to Enable the
Interaction between the Worker and the Industrial Robot
Case Study: One Worker Interaction with a Dual-Arm Industrial Robot
Ahmed R. Sadik
1,2
and Bodo Urban
1,2
1
University of Rostock, Universitätsplatz 1, 18055, Rostock, Germany
2
Fraunhofer Institute for Computer Graphic Research IGD, Joachim-Jungius-Str. 11, 18059, Rostock, Germany
Keywords: Flexible Manufacturing Paradigm, Holonic Manufacturing System, Multi-Agent System, Worker-Industrial
Robot Cooperation, Semi-Autonomous System, Human-Robot Interaction, Gesture Recognition.
Abstract: Involving an industrial robot in a close physical interaction with the worker became quite possible, as a result
of the availability of different collaborative industrial robots in the market. The physical cooperation between
the industrial robot and the worker usually done under the umbrella of the flexible manufacturing paradigm,
where both the industrial robot and the worker need to change their tasks fast and efficiently, to cope with the
changes in the manufacturing process. This means that a reliable manufacturing control system must stand
behind this physical interaction to achieve the proper communication interaction. A holonic control
architecture is an ideal solution for this problem. Therefore, during this research we study the most commonly
applied model of the holonic control architecture, then we apply this architecture on our case study, where
one worker cooperates with a dual-arm industrial robot to build and produce any new product. Also the
research uses the worker’s hand gesture recognition as a method to interact with the industrial robot during
the execution of a cooperative production scenario.
1 INTRODUCTION
Human-Robot Interaction (HRI) is the field of
research focuses on understanding, designing and
evaluating robotic systems which involve the human
as an essential element of these systems (Goodrich
and Schultz, 2007). The HRI fundamental rules are
originally based on a fictional speculation as stated by
author Isaac Asimov’s novel “I, Robot” (Pinker,
1999). The first two fundamental rules of the HRI are
as following:
A robot may not injure a human being or,
through inaction, allow a human being to
come to harm.
A robot must obey orders given to it by
human beings.
As a scientific interpretation of these rules. The
first fundamental is mainly addressing the problem of
a safe physical HRI, while the second rule addresses
the problem of HRI information communication.
Accordingly the term interaction in the context of
HRI can either mean physical or information
interaction. A very good example where the HRI
physical close proximity interaction is more dominant
than the information interaction is an exoskeleton
robotic suit. In an exoskeleton the human literally
wears a robotic suit to magnify his strength and
endurance (Fontana, 2014). On the other hand, a tele-
operated robot arm operates in a nuclear reactor
(Parker and Draper, 1998)
is an example of a remote
HRI where the information interaction is more
dominant than the physical interaction. However in
most of the HRI scenarios, both forms of interaction
coexist together, regardless which one is more
obvious or dominant.
The HRI has gained a great attention in industrial
applications in particular (Lasota et al, 2014). As a
result of this attention, a new generation of safe
cooperative industrial robots are now available in the
commercial market. Example of these robots are
KUKA lightweight, Rethink Baxter, YuMi ABB dual
arm, and Universal Robots. These robots apply
different technologies and methodologies to grantee
the human safety during a close proximity interaction.
Therefore, safe physical HRI is achieved. However,
there is no clear control architecture which specifies
the information communication interaction between
190
R. Sadik A. and Urban B.
Applying the PROSA Reference Architecture to Enable the Interaction between the Worker and the Industrial Robot - Case Study: One Worker Interaction with a Dual-Arm Industrial Robot.
DOI: 10.5220/0006191801900199
In Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), pages 190-199
ISBN: 978-989-758-219-6
Copyright
c
2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the safe industrial robot and the worker in a
cooperative production scenario.
In section 2 of this paper, we introduce some well-
known methodologies and terminologies which are
basic keys in Intelligent Manufacturing System (IMS)
design. It is important to know these methodologies
and terminologies in order to formulate the problem
statement in section 3. Section 4 explains the
technologies which are used to apply the solution
concept. Therefore an overview of the solution model
can be explained in section 5. Section 6 shows a case
study where we implement the solution concept.
Finally section 7 wraps up the work summary with
the conclusion and the future research.
2 BASIC KEY CONCEPTS
2.1 Flexible Manufacturing System
A Flexible Manufacturing System (FMS) is an
integrated system of machine modules and material
handling units which together can offer a certain
amount of flexibility (Lozano et al, 1994). A typical
FMS is composed of a series of manufacturing
workcells which controlled by a stand-alone
controller. A workcell can contain an automatic
machine, a material handling and storage station, an
industrial robot, or a human worker.
The flexibility term can be applied to different
aspects of the manufacturing system, such as: the
machine, the material handling and routing, the
control system, and the product building plan and
volume (Elmaraghy, 2005). The objectives of the
FMS are to increase the manufacturing system
reliability, optimizing the cycle time, reduce the lead
times and costs, overcome the internal changes like
breakdowns, and improve the worker productivity,
the machine efficiency, and the overall quality (Koren
et al, 1999; Kruger, 2015).
A Worker and Industrial Robot (W&IR)
cooperative workcell is a novel case study of the
FMS, where the worker can use the industrial robot
as an intelligent tool to achieve the FMS objectives.
The FMS control system could follow a centralized
or a distributed control topology. In a centralized
control topology, all the workcells controllers are
supervised by one central controller which carry out
the final decision making. In a distributed control
topology, the decision making task is distributed
between all the controllers. The scope of this research
focus on distributed control approach as a modern
intelligent method to control the W&IR cooperative
workcell.
2.2 Holonic Manufacturing System
In the late of sixties, the term holon has been
introduced for the first time by philosopher Koestler
(Koestler, 1967). Koestler developed the term as a
basic unit in his explanation for the evolution of
biological and social structures. Based on his
observations that organisms (e.g., biological cells) are
autonomous self-reliance units, which have a certain
degree of independent control of their actions, yet
they still subject to a higher level of control
instructions. His conclusion was that any organism is
a whole “holos” and a part “on” in the same time,
which derived the term holon (Giret, 2008). The
concept of holon has been adopted in the early of
nineties by the IMS consortium, to define a new
paradigm for the factory of the future. IMS has
defined the holon as an autonomous cooperative
building block of the manufacturing system, that can
be used to transform, transport, store and/or validate
the information and the physical objects (Radu and
Frank, 2006).
The Holonic Manufacturing System (HMS) is
basically a distributed control and communication
topology which divides the manufacturing process
tasks and responsibilities over different holon
categories. Two well-known reference models are
following the holonic manufacturing architecture
which are: Product-Resource-Order-Staff
Architecture (PROSA) model (Van Brussel et al,
2003), ADAptive holonic COntrol aRchitecture
(ADACOR) model (Leitao and Restivo, 2006).
The PROSA reference model implements three
basic holons as shown in Figure 1-a. These holons are
resource, product, and order holons. The resource
holon is a physical entity within the manufacturing
system, it can represent a robot, machine, worker, etc.
The product holon stores the process and the product
tasks needed to insure the correct manufacturing of a
certain product. An order holon is responsible for
assigning the tasks and making sure they have been
accomplished. The ADACOR reference model
(Leitao and Restivo, 2008) implements the same three
PROSA basic holons plus a fourth holon called a staff
or a supervisor holon. The supervisor holon is
providing coordination services when it is needed to
cooperate outside the boundaries of the workcell.
The holon generic structure is shown in Figure 1-
b. The resource holon is usually composed of two
components which are responsible for the physical
and communication interaction respectively. While
the other holons can have only the communication
component.
Applying the PROSA Reference Architecture to Enable the Interaction between the Worker and the Industrial Robot - Case Study: One
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Figure 1: (a) PROSA HMS Reference Model – (b) Holon
Generic Structure.
3 PROBLEM FORMULATION
Close physical interaction between the worker and the
industrial robot is a new trend in manufacturing. This
trend was proposed as a part of Industrie 4.0
recommendations as a new vision for the smart
factory of the future. Especially for the Small and
Medium-Sized Enterprises (SMEs), as they apply a
highly customized industrial processes which follow
the flexible manufacturing paradigm. Accordingly,
the worker existence alongside the industrial robot is
essential in an SME, as the worker can easily adapt to
the fast changes in the production requirements.
Simultaneously, the industrial robot is important
resource on the factory shop floor, as it is reliable in
terms of speed, load lifting, and accuracy, etc.
The first fundamental rule of the HRI can be
easily obtained in a W&IR cooperative workcell, due
to the existence of a variety of safe industrial robots
in the commercial market. The problem is to achieve
the second fundamental rule of the HIR, which is to
connect the worker and the industrial robot together
from the information communication point of view.
The PROSA reference model is a well-known
control and communication architecture which has
commonly used to solve different FMS problems.
However implementing the PROSA model over this
specific case of the FMS (i.e. a W&IR cooperative
workcell) is a new problem. The main purpose of
applying the PROSA model is to afford the worker
the privilege to use the industrial robot as an
intelligent tool within the production process. In a
semi-autonomous control manner, the control system
should allow the worker to interfere the production
process and teach the robot a new task. This new task
can be stored in the system and used as a building
block of more complicated products.
Furthermore, in order to complete the W&IR
interaction, the control system must be able to
recognize the worker activities. Therefore, the second
part of this research problem is to find an appropriate
recognition method which can be used to express the
different tasks done by the worker.
4 BASIC KEY TECHNOLOGIES
4.1 Autonomous Artificial Agent and
Multi-Agent System
A software agent is a computer system situated in a
specific environment that is capable of performing
autonomous actions in this environment in order to
meet its designer objective (Jennings & Wooldridge,
1998). An agent is responsive, proactive and social.
Responsive means that the agent can perceive its
environment and respond in a timely fashion to the
changes occurring in it. Proactive means that the
agent is able to exhibit opportunistic, goal directed
behaviour and take initiative. Social means that the
agent can interact with other artificial agents or
humans within its environment in order to solve a
problem.
Conceptually, an agent is a computing machine
which is given a specific task to execute. Therefore,
it chooses certain set of actions and formulate the
proper plans to accomplish the assigned task. The set
of actions which are available to be chosen by the
agent are called a behavior. The agent behaviors are
mainly created by the agent programmer. An agent
can formulate one or more plan to reach its target. The
selection of an execution plan among others would be
based on a certain criteria which has been defined by
the agent programmer. Building an execution plan is
highly depending on the information which inferred
by the agent from its environment. A Multi-Agent
System (MAS) is a collective system composed of a
group of artificial agents, teaming together in a
flexible distributed topology, to solve a problem
beyond the capabilities of a single agent (Shen et al,
2006).
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Figure 2: Java Agent Development (JADE) Framework.
JAVA Agent Development (JADE) is a
distributed MAS middleware framework (JADE,
n.d.). JADE applies reactive agent architecture which
complies with the Foundation for Intelligent Physical
Agent (FIPA) specifications, and provides a graphical
interface to deploy and debug a MAS (Bellifemine et
al, 2007). FIPA is an IEEE computer society
standards organization that promotes agent-based
technology and the interoperability of its standards
with other technologies (FIPA, n.d.). JADE agents
use FIPA-Agent Communication Language (FIPA-
ACL) (Poslad, 2007), to exchange messages either
inside its own platform or with another platform in a
distributed MAS as shown in Figure 2.
Each JADE instance is an independent thread
contains a set of containers. A container is a group of
JADE agents run under the same JADE runtime
instance. Every platform must contain a main
container. A main container contains two necessary
agents which are: an Agent Management System
(AMS) and a Directory Facilitator (DF). AMS
provides a unique ID for every agent under its
platform, to be used as an agent communication
address. While the DF announces the services every
agent can offer under its platform, in order to
facilitate agent service exchange, so that each agent
can obtain its specific goal (Caire, 2009; Teahan,
2010).
4.2 Hand Gesture Recognition
One of the most successful gesture recognition
techniques is the vision-based sensing that applies
active technique approach. In active sensing a signal
of a burst of (light, microwaves or sound) waves is
emitted, then the reflected signal by the surrounding
is received back by the sensor. Often the sensor is non
active as long as no motion occurs in its sensing
range, till some object moves within this sensing
range, therefore the change in the reflected signal
activates the sensor. The most famous sensors which
belong to this category are the Kinect and Leap
Motion (Berci and Szolgay, 2007).
Figure 3: (a) Leap Controller Dimensions – (b) Leap
Controller Coverage Range.
The Leap Motion controller (Leap Motion, n.d.) is
a sensor device that aims to translate the hand
movements into computer commands. The Leap
dimensions are 8 cm in length and 3cm in width, and
it can be connected to the computer using a USB
connection as shown in Figure 3-a. The controller
range of sensing is a hemispherical volume which
extends to of 60 cm in radius as shown in Figure 3-b.
Using two monochromatic IR cameras and three
infrared LEDs the device observes its sensing
volume. The infrared LEDs emit a 3D pattern of IR
light dots, simultaneously the cameras reconstruct the
reflected data in a rate of a 300 frames per second.
The constructed data transfers to the host computer
via the USB connection, where it can be parsed by the
Leap Motion controller software (Potter et al, 2013).
Applying the PROSA Reference Architecture to Enable the Interaction between the Worker and the Industrial Robot - Case Study: One
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4.3 Cooperative Industrial Robots:
Baxter Rethink Robot
The robot Baxter is a collaborative dual-arm robot.
Every arm is a 7 DOF articulated industrial robot.
Collaborative robotics is to share the process between
two robots or a robot and a human being, unlike the
conventional industrial robot that repeatedly
performs the very same task on the production line
without a direct interaction with the human coworkers
in the process. Baxter is slower than the traditional
industrial robot and is "compliant", i.e. its movements
are elastic, non-hazardous to the humans and closer to
that found in the nature. The objective of the
collaborative robotics, rather than performing a single
task constantly, which is the case of a conventional
industrial robot, is to be able to carry several small
tasks, alongside humans in its work environment.
Baxter is particularly suitable for SMEs that do not
have sufficient volume to automate sophisticated
tasks. But they can achieve higher productivity if they
automate many simple tasks (Rethink Robotics, n.d.).
The maximum payload of one of Baxter’s arm is
2.3 Kg. The two arms are programmable within the
same code. Baxter has mechanisms of "anti-self-
collision" for compensating its movements to avoid
collisions of the two arms. These mechanisms may be
deactivated in specific cases (passage of an object
from one hand to the other, handling two hands close
together). It is therefore possible to synchronize the
two arms movements to coordinate them together or
controlling any of them separately.
5 SOLUTION MODEL
While the PROSA is a conceptual model focuses on
HCS description, it does not specify a certain
technology to apply this concept. On the other hand,
artificial agent technology is a general purpose
solution which can apply the PROSA concept. Thus
during this research, JADE agent framework has been
used to implement the concept of the PROSA. Figure
4 illustrates the implementation of the PROSA basic
holons over a W&IR cooperative workcell. With the
assumption that a single worker cooperates with the
Baxter dual-arms in flexible manufacturing scenario.
On the worker platform, there are four different
holons, two of them are resource holons which locate
on the shop floor layer. The first resource holon is the
worker User Interface (UI). This worker UI holon has
a physical component which is the worker laptop. The
communication component of this holon is
implemented in the automation layer in the form of
Figure 4: Worker & Industrial Robot Flexible Cooperative
Scenario based on Holonic Manufacturing Concept.
the worker task display agent. The worker UI is
basically responsible for creating the new tasks either
for the worker or the robot, and constructing new
products. Also the worker UI displays the assignment
task for the worker during the product execution. The
second resource holon is the worker task execution.
The worker task execution uses the Leap
controller as a physical competent to capture the
worker’s hand gestures which are associated with
different work events. The communication
component of this holon is the worker task execute
agent. The last two holons on the worker side are the
product and the order holons, which only implement
communication components, therefore they are only
located in the automation layer in a form of
autonomous artificial agents.
On the robot platform, there are two resource
holons. The first resource holon is the robot display.
Similar to the worker UI holon, the robot display
holon has two components, its physical component is
the Baxter screen, which is connected to the robot
task display agent as its communication component.
The robot display holon displays the baxter task while
execution. The second resource holon is the robot task
execution. The physical component of this holon
could be Baxter right or left arm. In either cases the
Baxter arms are connected with the robot task
execution agent as their communication component.
The robot task execute holon can assign a task either
for the right arm, the left arm, or both.
The last holon in this solution model is the robot
task building resource holon. This holon is in fact
distributed over both the worker and the robot
platforms. This is because it is used by the worker to
teach one of the Baxter arms a new task. The worker
platform and the robot platform are communicating
with each other’s using a Wireless Local Area
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Network (WLAN) technology which deploys FIPA-
ACL Communication Protocol.
6 CASE STUDY
During this section, a simple product will be built and
executed using the previously mentioned solution
concept. The aim is to show as simple as possible how
the holons interact. Therefore, a product composed of
three consequent tasks will be constructed. The first
task will be assigned to the Baxter right arm, the
second task will be assigned to the worker, and the
final task will be assigned to the Baxter left arm.
Figure 5: A Case Study for a Three Tasks Product.
6.1 Product Building
Figure 5 shows the worker UI and the different steps
to build a new product. Figure 5-a shows the Main
App UI, which is mainly used to navigate to create a
new task either for the robot or the worker, and
create/execute a new product. Figure 5-b shows the
creation of new robot task via the robot task UI. In
our case study, two tasks are assigned to the Baxter.
Before the worker starts teaching the Baxter a new
task, he gives the task a name and selects which arm
will be used. Then presses start recording button to
physically teach the robot arm the new task,
ultimately the worker presses stop recording button
when finish teaching.
Figure 6: JADE Interaction to Teach a New Task for the
Baxter.
Figure 6 shows the communication interaction
between Baxter-Teach@WorkerPlatform and
Baxter_Teach@BaxterPlatform in order to teach
Baxter a new task. An INFORM message is sent from
the worker side in case of start/stop recording, and the
message is replied by a CONFIRM message from the
Baxter side to complete the handshaking process as it
can be seen in Figure 7. The start teaching message
holds the name of the task and which arm should
perform this task. Accordingly the task will be
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recorded on Baxter Platform using this exact task
name as a reference.
Figure 7: ACL-Messages exchange to start task recording.
Figure 5-c shows the creation of a new worker
task via the worker task UI. In our case study four
different gestures have been programmed to be
recognized by the Leap Motion Controller. Those
four gestures are Pick and Place an object, Horizontal
Swipe the hand from left to right, Lean the right hand
back, and finally a Tool (Screw Driver) recognition.
Swipe hand and Tool are pre-defined gestures by the
Leap SDK. However Pick and Place and Lean right
hand back are customized gestures by our code which
will be shown in the next section. Pick and Place has
been selected as an indication of the worker task.
After defining all the needed tasks from both the robot
and the worker to build a product. A product building
plan can be defined using the product UI as shown in
Figure 5-d.
6.2 Product Execution
After pressing execute product button in the Main
App UI, an ACL-Message with a communicative act
“AGREE” is sent from the Product Order agent to the
Order Execute agent as shown in Figure 8.
Figure 8: JADE Interaction to execute a Three Tasks
Product.
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The AGREE message is an indication to start the
execution of a new product. The message content
holds the task target and the task name as shown in
Figure 9-a. the Order Execute agent answers the
Product Order agent with a CONFIRM message the
same way described previously in section 6.1 during
teaching Baxter a new task. Based on the task target
within the AGREE message, the Order Execute agent
assigns a new task execution for that target. Therefore
it informs the task to the
Baxter_Task@BaxterPlatform and simultaneously to
the Task_Dispaly@BaxterPlatform.
Figure 9: ACL-Messages to start tasks execution.
Both the last mentioned agents send back
confirmations that they received a new task to the
Order Execute agent. When Baxter Task agent
receives an INFORM message to start executing a
new task, it searches into the message contents for the
task name. When Baxter Task agent finds a match of
the task name with the tasks previously recorded. It
starts to play back this exact task, and finally sends an
ACL-Message with communicative act “PROPOSE”
to the Product Order agent. The PROPOSE message
indicates that a task done from the product. As the
product tasks still not finished, the Product Order
agent sends an ACL-Message with communicative
act “ACCEPT-PROPOSAL” to the Order Execute
agent.
Figure 10: The Python Server Code to define the Worker
Hand Gestures.
The ACCEPT-PROPOSAL message that has
been sent to the Order Execute Agent is an indication
of a new task assignment. The same previously
mentioned mechanism to assign a new task for the
robot is followed to assign a worker task. As this time
the ACCEPT-PROPOSAL message content indicates
a Worker Task with type Pick and Place as shown in
Figure 9-b. Thus the Order Execute agent informs the
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197
task to the Worker-Task@WorkerPlatform and to the
Task-Dispaly@WorkerPlatform. Both the last
mentioned agents send back confirmations that they
received a new task to the Order Execute agent. When
the Worker Task agent receives an INFORM message
to start executing a new task, it extracts the task name,
initialized a unique socket, and waits the Leap server
to capture this specific gesture. The definitions of the
different gestures are written in the Leap Server using
Python language as shown in the Figure 10.
When the Leap server captures the specified
gesture by the Worker Task agent, it informs the
Worker Task agent that this gesture was detected.
Accordingly the Worker Task agent turns off the
connection socket and sends a PROPOSE message to
the Product Order agent to inform a task done event.
The Baxter Task-2 is achieved exactly the same way
Baxter Task-1 done as can be seen in Figure 9-c,
except one important difference. At the final step of
the product execution when the Baxter Task agent
sends a PROPOSE message to the Product Order
agent. The Product Order agent realizes that all the
product tasks have been fulfilled, therefore it sends
the Order Execute agent an ACL-Message with a
communicative act “REJECT-PROPOSAL”, to
indicate the end of this routine.
7 SUMMARY, CONCLUSION
AND FUTURE WORK
During this research, we introduced the basic
fundamental rules of the HRI. In the field of industrial
robots, the first fundamental rule has become easily
achievable, because of the availability of safe
cooperative industrial robots in the robotics market.
However to achieve the second HRI fundamental
rule, an intelligent control and communication
architecture must be found and implemented. The
purpose of this control architecture not only to
connect the industrial robot and the worker together
from the information point of view, but also to enable
the flexible cooperation between them.
The research has applied the PROSA holonic
control reference model to solve the addressed
problem. The PROSA reference model specifies the
three basic holons which are required in intelligent
manufacturing scenarios. That was the reason of
choosing this model specifically, as the scale of our
study still limited to one worker in cooperation with
one or two robots.
Based on the PROSA specifications, the physical
and information interaction model between a worker
and a dual-arm has been constructed. JADE
autonomous agent framework has been used to
implement and deploy the communication
components of the PROSA holons. A robot task
teaching resource holon has been used by the worker
to teach any of the Baxter arms a new task. While a
robot task execution resource holon was responsible
for executing this task. The Leap sensor has been
chosen to track the gestures of the worker’s hand,
accordingly a dedicated resource holon was assigned
to monitor the Leap Motion controller server and pass
the gestures to this resource holon when it is needed.
As has been shown in the case study, the
production flexibility has been accomplished using
the proposed solution. Thus, the worker could teach
the robot many different tasks at any time during the
manufacturing process. Simultaneously, he can
assign different hand gestures which are associated
with his activities during the production process.
Ultimately, the worker can use different combinations
of his hand gestures along with the robot tasks which
are stored in the system to construct more
complicated production routines. Accordingly, the
proposed solution can increase the manufacturing
system reliability by adapting to the production
requirements, reduce the final production lead time
and cost, and improve the worker productivity and the
robot efficiency.
As the coordination between two or more than
worker and industrial robot is out of context of this
research, a staff holon has not been implemented.
However, in the future work the proposed solution
can be scaled over more than one worker and
industrial robot. Therefore the staff holon can be
taken into consideration. Also during the future work,
more hand gestures can be investigated and studied,
thus it can be offered to the worker to build more
sophisticated products.
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