Page - 37 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics
Image of the Page - 37 -
Text of the Page - 37 -
a minimal configuration such that the robot can perform a
capability.
Another method which uses an ontology to describe the
environment was presented in [9]. The method uses a de-
scription of the environment which is based on an ontology
together with a description of skills the robot can perform.
Using this description, the robot can plan a sequence of skills
which need to be executed to perform a certain task. Such
a task was presented in [10] where the robot had to place
parts of an industrial kitting operation.
To plan which robot can perform which task in a het-
erogeneous group of robots the method outlined in [11]
can be used. The method defines capabilities which have
preconditions which need to be met to allow the execution
as well as information which need to be provided by the
robotic system to allow the capability to be executed. Thus,
the robot can plan which capabilities need to be executed to
perform a task. Furthermore, the robot can use the hardware
description to check if such an execution is possible. As
many capabilities, e.g. grasp an object, may only work under
certain restrictions, e.g. size of the object, one can add an
approximation description to each capability which defines
which conditions need to hold approximately to allow the
execution of the capability. This allows to define capabilities
in more detail and thus allow a better distribution of tasks
among the robotic group. The method outlined in [11] focus
on distributing tasks in a group of robots whereas the tool
we present in this paper focuses on the configuration of
the robotic system during the design phase. Thus, instead
of planning which capabilities to use to fulfill a task, we
show which different minimal configurations of the robotic
system allow the robot to execute the capability. This allows
the developer of the robotic system to choose the best fitting
set of capabilities.
The method presented in [12] extends AutomationML [13]
to allow the modeling of a robotic system. This is done by
extending the given concepts with robotic specific concepts
such as actuators or sensors. Furthermore, the method allows
an automatic conversion of AutomationML specifications
into an ontology which can be used to check for consistency.
This can be used to model a robotic system with Automa-
tionML and afterward check through the transformation to
an ontology if every dependency is met or if a component
is used which does not solve any given task. Beside these
checks, further checks can be applied to verify that this
system can be realized with the help of ROS [14]. This
allows the developer to ensure that the modeled robot can
be realized. After these checks are performed one can apply
a model-to-text transformation to generate code stubs which
ensure proper communication and operation per the modeled
system. This allows a faster creation of a robotic system
following a model-based approach like the method outlined
in [2]. In contrast to our approach, the method does not offer
the possibility to check which components are necessary to
perform a capability. Thus, the method presented in [12] is
alsonotable to specifyaminimalconfigurationwhich fulfills
the need for a specific capability as our tool can. Besides the description of tasks for configuration, such a
description is also often used to assign a task, e.g. in a multi-
robot system. One such example is which uses an ontology
to assign tasks is presented in [15]. The method uses an
ontology per robot to describe which roles can be performed
and how these roles are performed. Additionally, tasks are
described in the ontology and how they can be executed
through a role which is assigned to a robot. The system uses
this ontology to find a matching robot and assigns different
roles for different robots to fulfill the specified task.
VI. CONCLUSION AND FUTURE WORK
The proper configuration of a robotic system for a given
task is a time consuming and tedious task. Especially one
needs an expert to perform this task to consider all possibil-
ities as well as all dependencies. To address this problem, in
thispaperwepresenteda tool for theautomaticconfiguration
of a robotic system for a given task. The tool uses an
ontology-based knowledge base, allowing to reuse publicly
available knowledge bases, to describe which dependencies,
exist between a task and software and hardware components.
Furthermore, we have presented a method to derive a min-
imal set of software and hardware components to fulfill a
certain task. This allows the user to simply find a possible
configuration of the robotic system, that allows the robot to
fulfill its task. To allow an easy interaction the tool has a
graphical user interface which allows the user to select tasks
as well as used components. Thus, the user can specify the
currently used components on the robot to check if a new
task can be achieved by the robot, or which components need
to be added to allow the robot to achieve a given task.
Currently, the tool can only be used by a human to decide
which components to use to allow the execution of a specific
task. It is left for future work to allow the robot itself
to use the tool. This would open the possibility that the
robot finds alternative solutions to a task during runtime.
Thus, the robot could reconfigure itself to react to a fault or
changes in its task. Furthermore, currently only a minimal
number of components is searched for the configuration,
neither computation costs nor investment or development
costs are considered in the configuration. It is left for future
work to integrate these costs to allow to find a configuration
which minimizes the computation effort or to minimize the
investment costs.
REFERENCES
[1] Z. Zhang, “Microsoft kinect sensor and its effect,” IEEE multimedia,
vol. 19, no. 2, pp. 4–10, 2012.
[2] G. ”Steinbauer and C. Mu¨hlbacher, “Hands off - a holistic model-
based approach for long-term autonomy,” in Workshop on AI for Long-
Term Autonomy, 2016 IEEE International Conference on Robotics and
Automation (ICRA).
[3] Knowrob.org. Capability ontology - knowrob. [Online]. Available:
http://knowrob.org/kb/srdl2-cap.owl
[4] Apache.org. Jena framework - apache. [Online]. Available:
https://jena.apache.org/
[5] choco solver.org, “choco-solver.” [Online]. Available:
http://www.choco-solver.org/
37
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Title
- Proceedings of the OAGM&ARW Joint Workshop
- Subtitle
- Vision, Automation and Robotics
- Authors
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wien
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände