=Paper=
{{Paper
|id=Vol-3214/WS3Paper3
|storemode=property
|title=PlugBot Architecture for Modular Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-3214/WS3Paper3.pdf
|volume=Vol-3214
|authors=Georg Weichhart,Markus Ikeda,Matthias Propst,Roman Froschauer
|dblpUrl=https://dblp.org/rec/conf/iesa/WeichhartIPF22
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==PlugBot Architecture for Modular Manufacturing==
https://ceur-ws.org/Vol-3214/WS3Paper3.pdf
PlugBot Architecture for Modular Manufacturing
Georg Weichhart1, Markus Ikeda1, Matthias Propst1 and Roman Froschauer2
1
PROFACTOR GmbH, Im Stadtgut D1, 4407 Steyr, Austria
2
University of Applied Sciences Upper Austria, Wels, Austria
Abstract
PlugBot was an Austrian research project (2019-2021) in which an architecture to integrate
heterogeneous (robotic) systems has been developed. Modeling system capabilities for task
execution as atomic skills is a concept that allows semantic unification for orchestration of
multiple skills along workflows as well as hardware abstraction and implementation agnostic
workflow modelling without taking concrete parameters of the executing devices into ac-
count. The architecture and approach has been evaluated in multiple use cases in the manu-
facturing domain. Future work will include researching the applicability of the concept for
mobile robots.
Keywords 1
Architecture for integration, enterprise interoperability, robotics, skill-based programming
1. Introduction
Enterprise Interoperability has its roots in research fields Enterprise Modelling, Enterprise Archi-
tecting and Enterprise Integration [1]. It is a model driven research domain, where multiple interacting
systems are supported in their communication, coordination or collaboration [2]. These fields research
the interaction of systems from an information systems point of view, where systems include human
and artificial agents that interact [3]. To distinguish interoperability and integration, we can take a
look at a continuum, where the interoperability end aims at a loose coupling and the other end (inte-
gration) at tight coupling of systems. A system is an integrated unit, in contrast to that, a system-of-
systems is a loose coupled unit where the systems have a great degree of autonomy but still the inter-
action needs to be interoperable [4]. Systems-of-systems are modular systems which – from a theo-
retical point of view - are very flexible due to the autonomy of the systems. In applied production
research, the systems-of-systems approach supports decentralized and modular manufacturing. Here
recently cyber-physical systems (CPS) research has been developed as an important paradigm form-
ing Cyber-Physical Production Systems (CPPS) [4]. In production in general, but more specifically in
robotics, interoperability is becoming an important aspect that needs to address technical interopera-
bility, semantic interoperability and physical interoperability. This latter part requires more attention
as it is often overlooked in enterprise interoperability, discussing only information systems and flows
of data / information / knowledge.
Within the PlugBot project an architecture and framework has been researched that enables modu-
lar manufacturing with special attention to robotics. In the following section, we discuss the state of
the art, followed by a description of obtained research results and an example use cases, showing the
potential of the solution. Finally, we discuss concluding remarks.
2. State of the art
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: Georg.Weichhart@profactor.at (G. Weichhart); Markus.Ikeda@profactor.at (M. Ikeda); Matthias.Propst@profactor.at (M. Propst);
roman.froschauer@fh-wels.at (R. Froschauer)
ORCID: 0000-0002-1405-5825 (G. Weichhart); 0000-0001-6754-5783 (M. Ikeda); 0000-0002-7724-9037 (M. Propst); 0000-0001-6952-
285X (R. Froschauer)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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In this section we first, present trends and the state of the art of modular manufacturing system,
give special attention to skill-based robotic systems and then discus how modular systems can be
made interoperable.
2.1. Challenges in modular cyber-physical production systems
A system is a (conceptual) unit that consists of modules. It has an interface to its environment and
can therefore be distinguished from it. Through that interface a system interacts with the environment
and other systems [5, 6]. A system has a purpose, or goal. To realize that purpose, the modules pro-
vide required functions. These modules do not work independently but are dependent on the system to
which they provide the function. In contrast to that, a system has a certain level of independence.
A Cyber-Physical System (CPS) is a system, which consists of a physical and a cyber module, re-
alizing the required functionality. The interfaces of such systems are sensors and actors that support
interaction. In addition to this, the software often supports the networking of such systems and the
communication and collaboration with other systems. Multiple of such systems form Cyber-Physical
Production Systems in the manufacturing domain forming a System-of-Systems (SoS) [4].
In the manufacturing domain, also the shop floor may be addressed as an integrated system or a
system-of-systems consisting of loose coupled systems. The difference lies in the structural modulari-
ty, which allows more adaptiveness and flexibility [7]. Of course, structural aspects can only provide
the basic possibilities [8]. One way to implement a modular and dynamic system is given through the
Multi Agent System (MAS) architecture [9, 10] .
A challenge to approaches that enable such modular and agile CPPS is the disintegration of the
Automation Pyramid, which is the currently implemented architecture. This hierarchical organization
of IT systems is based on the planning horizon implemented by the individual systems. In existing
manufacturing systems it’s hard to move towards more modular and agile manufacturing.
One of the challenges is, that the Automation Pyramid is often implemented as integrated system
where every layer is strongly linked to the next and previous layer. In order to reach a new improved
modular and flexible architecture the paradigms of loose coupling and interoperability are necessary.
2.2. Skill-based modular robotics
Skill-based modeling and operation of industrial software & automation systems including robots
is widely researched as accepted method for enabling hardware agnostic programming and loosely
coupling of automation components towards plug & produce in production plants [11-13] . The mod-
eling of skills is mostly based on (1) the definition of machine capabilities as a set of specific proper-
ties and the ability to perform specific operations on a process resource and (2) the externally trig-
gered execution of these operations as presented in [14-15] . By this means skill-based programming
deals with modeling of a subsequent order of executing specific process functions (i.e. skills) without
specific knowledge of its implementation on the machine or robot. Implementing these concepts sev-
eral approaches of skill-based programming have been applied in the field of robotics [13, 16] where
the functionality of a complex machine featuring several robots has been split into modules represent-
ing different capabilities (i.e. executable skills such as Grasp, Move, Grind, Drill, …) to allow for a
more generic way of programming, not relying on specific communication interfaces between the
modules (i.e. robot, gripper, machine). Today the demand of a generic communication method to call
skills is increasingly fulfilled by the OPC UA standard. In respect to robotics, there is a companion
specification available regarding the basic capabilities of the robot [17], as well as the well accepted
idea of representing capabilities, and its corresponding skills, using the OPC UA information model,
method calls or programs [18]. Furthermore, using OPC UA and its Local Discovery Services [19],
devices can be automatically discovered within a local network. As presented in [20], the production
process is operated by using recipes representing a sequence of skills to be executed by loosely cou-
pled and modular devices. Another implementation for a modular robotic application using hardware
from different vendors has also been presented in [21].
2.3. Enterprise interoperability in manufacturing
Enterprise Interoperability may be seen as one end of a continuum ranging from tight integration
of systems to loose coupled systems [22]. The loose coupling is a challenge. From a systems design
and engineering point of view, integrated systems engineering is very different to system-of-systems
engineering [4, 23]. In interoperability, both properties have to be enabled, independency of self-
contained systems that have a certain degree of autonomy, and seamless interaction where these sys-
tems communicate, coordinate and collaborate to reach a common, higher goal [24, 25].
Process models can be used to orchestrate independent engineered resources (including robots) [7,
26, 27]. These models help to increase interoperability on the organizational level. In addition to this,
the technical and the semantic layer need to be addressed to cope with the interoperability challenge.
The level of independence between systems is dependent on the use of standards on technical level
(e.g. OPC UA). Such standards provide clear interfaces between systems. The second level that must
be considered is the semantic interoperability layer, where data semantics needs to be understood by
all participants that use a certain interface [22].
3. PlugBot architecture for manufacturing systems-of-systems
PlugBot was an applied research project in Austria, running from 2019-2021. The overall goal was
to provide tools for the development and integration of heterogeneous (robotic) automation systems.
The targeted application domain is production and manufacturing. This section introduces the archi-
tecture and briefly discusses the two use cases that evaluate the functionality of the PlugBot approach.
3.1. Architecture
As explained in [14] “for seamless interaction of high-level control units and low level devices on
the network, logical and behavior level, a standardized interface and interaction concept is crucial”.
This also applies to heterogenous robot systems consisting of multiple devices and user interfaces.
Figure 1 shows the abstract PlugBot system architecture.
Figure 1: PlugBot System Architecture.
At the lowest level, the hardware layer, interfaces to hardware components are provided including
proprietary software modules (e.g. on-board robot control and programming). The device program-
ming layer encompasses the implementation of modular, reusable, type specific functionalities and
realizes the abstraction based on system skills (see above). These software modules utilize the HW /
SW systems of the hardware layer to apply the skill functionality in a process (step). In order to make
these functionalities a) configurable, b) executable, and c) monitorable in a standardized way, a uni-
form interface for integration was developed in PlugBot and described in our previous work [14]. This
interface follows the paradigm of skill-based programming. Hardware independence can be achieved
through implementation of the standardized interface by a device from a new vendor.
The skill-based interface is implemented at the interface layer. This layer contains the communica-
tion via OPC UA, as well as software libraries, which facilitate and standardize the realization of
skills and their integration as independent PlugBots. The interface layer deliberately uses OPC UA as
a platform independent standard, fostering from its built-in features (discovery, secure communica-
tion, quality of service, semantic data models), to realize an integrated, modular system.
Workflows are executed on the runtime layer and represent sequentially chained skills. The execu-
tion of individual skills can also be triggered through third-party systems that act as OPC UA clients.
Logical linking of a) a created workflow and b) the concrete PlugBot instances involved in the execu-
tion of this workflow (hardware mapping) is done within the asset layer. The basic idea is that a work-
flow can be created independently of the existing PlugBots. A logical assignment of concrete skill
executions to PlugBots is done in a separate step. Finally, the tool layer covers development tools for
workflows, workspace configuration (hardware mapping) and also application specific user interfaces
(GUIs).
The next section describes an example use case of the developed system architecture for the appli-
cation field "Mobile Manipulator". Note that application-specific architectures basically follow the
generalized system architecture according to Figure 1, but do not necessarily include all its elements.
3.2. Application use case
Pick-up and delivery robots combine multiple systems with specific skills (transport, manipula-
tion). Interoperability of robots with other machines enables automatic machine operation.
For the production of optical storage media, so-called stamper (dies) which contain the digital con-
tent of the customer's data are required and have to be transported between automatic storage systems
and production machines. In order to maintain productivity a mobile manipulator consisting of an
articulated robot (for object manipulation) as well as a mobile platform (to provide transportation
skills within the plant) is intended to take over this task. Human operators are informed using smart-
watch-based notification systems and take over complicated (de-)installation tasks of the stampers.
A second use case considers the assembly of customer-specific geared motors, where components
have to be retrieved and moved to the assembly. To justify the equipment costs for mobile manipula-
tors it is crucial utilizing the manipulator during transportation time for commissioning components
(e.g. roller bearings) from delivery containers to dispensing units. A robot pick and place skill is de-
ployed in parallel to the transportation skill of the mobile robot technically robust, safe and legally
compliant.
Figure 2: Mobile Manipulator System.
Figure 2 summarizes the load, unload and stock manager (part storage status tracking) skills for
the articulated robot (implemented in and organized by XROB [28]), a movement skill for the MiR-
type mobile robot, skills for operator notification as well as a JobDataBase skill to take over commu-
nication with a superior MES.
4. Conclusion
The PlugBot project focuses on the integration of robotic tasks in workflows that involve humans
and multiple machines. Skills are used as a conceptual abstraction that support and facilitate the
hardware agnostic integration and programming of systems. The developed architecture and use case
implementations provide interoperability on technical level by using the standard OPC UA interface
technology. Semantic interoperability is given by skills that serve as a unification concept on task
level. Using a workflow engine supports the orchestration of the skills and therefore serves as interop-
erability tool on process and organizational level. The presented concept was implemented and evalu-
ated in multiple use cases that showcase the practicability of the approach.
Two major findings were gained during the realization of the showcases by industry and research
partners of the PlugBot consortium. Firstly, the developed skill-based (programming) interface helped
to put focus on the implementation of skill behavior and workflows rather than dealing with integra-
tion aspects. Additionally, the skill implementation turned out to be less error prone due to standard-
ized interfaces. Secondly, the PlugBot architecture is applicable to different application areas (e.g.
mobile care systems, traffic management, grid management systems), targeting different heterogene-
ous systems that need to interact.
In an initial next step, the project ROBxTASK hast started, where focus is on a process modelling
and workflow engine. It also contains skills as abstraction layer. End-users should be enabled to pro-
vide process descriptions that can then be executed by multiple human and artificial agents [29].
In more distant, future work, a superior system with automatic resource allocation and scheduling
using a multi agent systems approach is planned [27]. The PlugBot concept will be applied to teams
of mobile (robotic) platforms in order to reduce the workflow programming and debugging effort. To
foster increased autonomy of the mobile robotic platforms (conceptualized as agents), further investi-
gation of the PlugBot standardized interfaces is required to assess the applicability to realize typical
multi agent system (MAS) functionalities like negotiation.
5. Acknowledgement
The research leading to these results has been developed in the “Plug and Produce Robotic Building
Blocks” (PlugBot) project (Grant Nr. 874293) and the ROBxTASK project (Grant Nr.: 880866). Both
projects have been funded by the Austrian Federal Ministry for Climate Action, Environment, Energy,
Mobility, Innovation and Technology (BMK) and the Federal Ministry for Digital and Economic Af-
fairs (BMDW) through the Austrian Research Promotion Agency (FFG).
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