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robot (rB) helps rA by picking up the
cup and placing it in a position that rA
can reach [6].
To correctly carry out the task, the
two robots should have the same
knowledge about the environment, its
entities, and each other's capabilities.
They also need a common vocabulary
to share this information. In particular,
they need the concepts and relations
standardized in the AuR standard and
in CORA [1]. For example, they need
the CORA concept of robot, which
depicts the features of each agent
involved in the use case. RobotGroup
defines the robots as a group that must
cooperate to fulfill the mission while
plan describes the set of actions that
each robot should perform to accomplish
its own assignment. Finally, every
decision is communicated to the other
robot through a communication element
that guarantees cooperation and collaboration.
From POS [1], the case study
inherits the concepts of position, orientation,
and pose measures useful to
describe the location of objects in the
world. From the current standard, interaction
models the robot cooperation
between each other and with the objects
of the environment. For example, when
rB is detecting the cup, an indirect information
interaction arises between the
robot and the cup. When it picks up the
cup, a direct physical interaction arises.
Object-centered environment, instead,
represents the physical object whose
location is the spatial area that robots can
potentially reach. It includes the set of
material components that are the objects
participating in the event (e.g., the cup,
the shelf, and the table). In this context,
every robot is equipped with an objectcentered
environment description of the
environment in which it is operating:
the table setup for rA and its surroundings
for rB. Such characterizations will
be exhaustive enough to make robots
cooperate when sharing tasks and space.
Finally, multiple examples can demonstrate
the need for manifested behavior.
When navigating, the manifested behavior
of rB includes the change of both its
location and spatial relationships. It also
depicts the quality of the robot sensors,
which may change during the exploration
of the environment. When rB picks
and places the cup, its manifested
behavior includes the evolution of both
the robot's location quality and the spatial
relationships. It also models the
action of getting in contact with the
item, maintaining this contact during
navigation, and losing it when the robot
puts the object on the table. Similar
considerations characterize the manifested
behavior of rA.
Conclusion and Current Status
One of the first benefits of the aforementioned
IEEE standard has been the
high level of cooperation among different
sectors such as academia and industry
to come up with a common ground
to describe AuR, zooming in from a
general standard on robotics CORA on
the specificities of the autonomous
robot's domain.
The IEEE development process has
allowed us to share different viewpoints
and to identify the features and the
essential components to describe autonomous
robots. As a result, a specific
ontology for AuR has been developed,
paying attention to the structural,
behavioral, and functional aspects of
this kind of systems. This ontology sets
the ground for future specifications of
the requirements and tasks to be fulfilled
when describing and designing
any autonomous robot.
As a final part of the developing
process of IEEE standards (SA), in
April 2021, voting members of the
working group voted to move the draft
to SA ballot. Shortly after, voting members
of the IEEE Robotics and Automation
Society Standards Committee
voted to move the draft to SA ballot.
Invitations were sent to participants
who indicated an interest in IEEE
myProject for the project to receive ballot
invitations and other notifications.
After that, individuals enrolled in the
ballot group to
participate in
the SA ballot.
In 2021 May, a
mandatory editorial
coordination
was completed
prior to
the initiation of
the SA ballot
in June 2021.
The working
group has been
polishing the
archit e c ture
and vocabulary to publish the standard
by the end of 2021.
As a result, a specific
ontology for AuR
has been developed,
paying attention
to the structural,
behavioral, and
functional aspects of
this kind of systems.
Acknowledgments
We wish to thank the following AuR
group members who also joined the
group's discussion: Daniel Beβler,
Abdelghani Chibani, Edison Pignaton
de Freitas, Amélie Gyrard, Alaa Khamis,
Chris Nowak, João Quintas, and
Joanna Isabelle Olszewska.
References
[1] IEEE Standard Ontologies for Robotics and
Automation, IEEE Standard 1872-2015, Apr. 10,
2015, pp. 1-60.
[2] N. Guarino, D. Oberle, and S. Staab, " What is
an ontology? " in Handbook on Ontologies. Berlin:
Springer-Verlag, 2009, pp. 1-17. doi: 10.1007/9783-540-92673-3_0.
[3]
A. Olivares-Alarcos et al., " A review and comparison
of ontology-based approaches to robot autonomy, "
Knowledge Eng. Rev., vol. 34, p. e29, 2019.
doi: 10.1017/S0269888919000237.
[4] C. Masolo, S. Borgo, A. Gangemi, N. Guarino,
and A. Oltra-mari, " Wonderweb deliverable d18
ontology library (final), " ICT Project, vol. 33052,
p. 31, 2003.
[5] I. Niles and A. Pease, " Towards a standard upper
ontology, " in Proc. Int. Conf. Formal Ontol. Inf. Syst.Vol.,
2001, pp. 1-9. doi: 10.1145/505168.505170.
[6] M. Diab, A. Akbari, M. Ud Din, and J. Rosell,
" PMK-A knowledge processing framework for autonomous
robotics perception and manipulation, "
Sensors (Basel), vol. 19, no. 5, p. 1166, 2019. doi:
10.3390/s19051166.
[7] J. Rosell, A. Pérez, A. Aliakbar, Muhayyuddin,
L. Palomo, N. García, " The Kautham project:
A teaching and research tool for robot motion
planning, " in: Proc. IEEE Emerg. Technol.
Factory Automat., 2014, pp. 1-8. doi: 10.1109/
ETFA.2014.7005143.
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