=Paper=
{{Paper
|id=Vol-2623/paper5
|storemode=property
|title=Structure of Multifunctional Cooperative Robotics System based on the Ontological Approach
|pdfUrl=https://ceur-ws.org/Vol-2623/paper5.pdf
|volume=Vol-2623
|authors=Tetiana Hovorushchenko,Artem Boyarchuk,Oleh Borovyk,Dmytro Medzatyi,Mykola Krasovskyi
|dblpUrl=https://dblp.org/rec/conf/intelitsis/HovorushchenkoB20
}}
==Structure of Multifunctional Cooperative Robotics System based on the Ontological Approach==
https://ceur-ws.org/Vol-2623/paper5.pdf
Structure of Multifunctional Cooperative Robotics
System based on the Ontological Approach
Tetiana Hovorushchenko 1[0000-0002-7942-1857], Artem Boyarchuk 2[0000-0001-7349-1371],
Oleh Borovyk 3[0000-0003-3691-662X], Dmytro Medzatyi 4[0000-0002-1879-2945]
and Mykola Krasovskyi 5[0000-0003-0374-338X]
1, 4, 5 Khmelnytskyi National University, Khmelnytskyi, Ukraine
2 National Aerospace University "Kharkiv Aviation Institute", Kharkiv, Ukraine
3 National Academy of the State Border Guard Service of Ukraine named after Bohdan Khmel-
nytskyi, Khmelnytskyi, Ukraine
1 tat_yana@ukr.net
2 a.boyarchuk@csn.khai.edu
3 bov_nadpsu@ukr.net
4 medza@ukr.net
5 toalegion@gmail.com
Abstract. The conducted analysis of the known methods and decisions in the
field of cooperative robotics shows that with a considerable number of effective
decisions in the industry there are still a number of unresolved issues that are
difficult to formalize and require the use of artificial intelligence components to
solve them, among which the most important are the need for the representation
of joint production environment, and the need to ensure that the relevant
knowledge and information is clear for the Cobot(s). In this paper, the structure
of the multifunctional cooperative robotics system based on the ontological ap-
proach is proposed. This system is based on ontology-based intelligent agents
(OBIAs), all joint knowledge of the system is represented as ontologies. The
developed structure of the multifunctional cooperative robotics system based on
the ontological approach considers both cobots and humans as actants of the
cooperative joint production environment, whereby they have access to all the
tasks, messages, and joint knowledge of the multifunctional cooperative robot-
ics system. The use of ontologies provides the following advantages for the de-
veloped system: processing of complex tasks and "understanding" of natural-
language tasks and messages; planning and analysis of the information by each
agent and the cobot; increasing the effectiveness of agents' and cobots' deci-
sions; formalization of the "common sense" semantics; accelerating and im-
proving the quality of processing natural-language information.
Keywords: Multifunctional Cooperative Robotics System, Cooperative Robot-
ics Industry, Cooperative Joint Production Environment, Joint Knowledge, On-
tology-Based Intelligent Agents (OBIAs).
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). IntelITSIS-2020
1 Introduction
The use of autonomous industrial robots is now a key issue for Industry 4.0 [1]. Co-
operative robotics is a new branch of industrial robotics that enables joint production.
Cooperative production is largely dependent on the presence of a cooperative (collec-
tive, collaborative) robot (cobot). Cobot is a variant of an industrial robot equipped
with a system of sensors and computer vision, which allows with a high probability of
preventing a cobot from colliding with a person and obstacles. Such robots are in-
tended for use in close collaboration with persons and next to people. The cooperative
robot must have the following features: no need in integration (the whole system must
be workable immediately); no need for programming or training (the robot must be
ready to work in minutes); wide functionality; advanced logic for work considering
the environment, including automatic disaster recovery; ensuring operational safety
(even in a collision at full speed) [2]. The primary task of the cobot is to help solve
complex non-automated tasks. Cobots change the concept of automation from fully-
automated operations to semi-autonomous operations, where the employee's decisions
will influence the actions of the cobot and vice versa [3].
To date, cobots have already been used successfully in the food industry (for ex-
ample, the Mjolkursamsalan Ayureyri dairy plant (Iceland)), in electronics manufac-
turing (for example, at Paradigm Electronics (Canada)), in the automotive industry
(for example, in cars assembly at SEW-Eurodrive in Baden-Württemberg (Germany),
in the sewing industry for automated sewing (startup Sewbo), in the machine-
building, in the metalworking industry, for packaging and palletizing, loading and
moving, for product quality checks, in connection and telecommunication (startup
Creating Revolutions), even for robotic massage [4]. The promising startups in which
the robotic arm have a related role (robot-barista, etc.) are also popular. However, the
potential field of their application is much wider – all types of production (including
light and food industry), office work, social sphere [5].
Creating cooperative robots that work in close contact with humans is a rapidly
evolving trend. The actuality of the development of such robots is due to the need to
assist people in performing heavy work in industrial enterprises and daily chores in-
home, care for the elderly and disabled persons, medical care in hospitals and post-
operative rehabilitation, providing leisure and training. Cooperative application,
which is natural for such tasks, implies absolute safety, high functional flexibility and
autonomy of the used robots. Such cooperative application requires the development
of new technologies in the field of management, the creation of new design solutions,
the development of algorithms for planning and execution of movements that ensure
the safety of physical interaction between people and cobots [6].
Considering that the cobot market will predictably reach $ 12303 million to 2025
from $ 710 million in 2018, i.e. it will increase by 50.31% during 2018-2025 [5], the
actual task now is rational planning and improving the quality of analysis of the in-
formation, which humans and cobots exchange in the joint environment, as well as
improving the effectiveness of the decisions of persons and cobots. The solve of this
task can be accomplished by developing the multifunctional cooperative robotics
system or technology based on the ontological approach, which is the purpose of this
research.
2 Literature Review
The cooperative robot shall meet the requirements of International Standards, the
main one being ISO/TS 15066:2016 [7], which is, in fact, a technical specification, in
particular, of the safety requirements for the cobots, for the interoperability of indus-
trial robotic systems and the work environment. This standard is a guide to assessing
the risks involved in working together with humans and cobots. According to [7],
there are 4 forms of joint work (teamwork): Safety-rated monitored stop – it's used
when the robot operates largely independently, but sometimes a person can enter in
robot's workspace; Hand guiding – it's used for precise operations with heavy objects;
Speed and separation monitoring – it's used for simultaneous human and robot opera-
tion; Power and force limiting – it's also used for simultaneous human and robot oper-
ation, with the force and power of the robot being controlled so that accidental contact
between the robot and the operator will not cause harm.
In cooperative robotics, both humans and robots perform tasks on the same product
in a joint workspace, but not simultaneously. A cooperative joint production work-
space (environment) is an environment where a robot and a person can perform tasks
simultaneously while working automatically [8, 9]. The most important challenges of
the cooperative environment are summarized in Figure 1 [9].
Let's analyze the literature to find known methods and solutions for the field of co-
operative robotics – Figure 2.
Fig. 1. The most important challenges of the cooperative environment [9]
Fig. 2. The known methods and solutions for the field of cooperative robotics
All components of the cooperative system communicate and exchange their
knowledge, process general information. This knowledge and information are usually
provided in a natural language that should be understood by the cobot. Using ontolo-
gies provides comprehensibility of such knowledge and information to the cobots.
Ontology is a collection of concepts which able to model terms of vocabulary into
domain knowledge. Ontology provides a better understanding of contextual
knowledge. From the perspective of computational science, ontology is defined as a
concept to model the system structure. For example, the relevant entities and relation-
ships that exist from observations are useful for specific purposes. Ontology associat-
ed with discovering and modelling reality under particular perspectives. It focused on
the structure and nature of an object. Ontology also referred to a representational
knowledge. Ontologies can describe abstract things (work processes, knowledge or
tasks) as well as real things (devices) [21-23].
Let's analyze the known ontological models for the field of cooperative robotics.
Thus, [9] presents a multi-agent system based on ontologies for a cooperative envi-
ronment. In [3], an ontology-based architecture is proposed as a solution for sharing
and transferring the knowledge, which are necessary to achieve complex scenarios of
employee-cobot interaction. The authors [24], using ontological engineering, cloud
library and semantic technologies, have developed a robotized ontology-based surgi-
cal care system. The semantic analysis technology Compreno, introduced by ABBYY
in April 2014, which is based on ontologies, that formalize the description of the en-
tire set of terms and relationships between them, enables cobot to understand natural
language in a cooperative joint production environment [25].
The conducted analysis of known methods and decisions in the field of coopera-
tive robotics indicates that nowadays there are still a number of unresolved issues:
• how can the cooperative robotics system handle more complex tasks that can be
solved by only humans?;
• how to ensure the reliability of the physical cooperative robotics system in the real
world?;
• how to more rational plan and analyze the information, which is received by each
individual robot (and then are made their decisions more efficient)?;
• how to organize easy human intervention in the cooperative robotics system ac-
cording to needs?;
• how to formalize the "common sense" semantics?;
• what form should be used to represent the cooperative joint production environ-
ment and components?;
• how to speed up and improve the quality of natural-language information pro-
cessing?
All of these unresolved issues are difficult to formalize and require the use of arti-
ficial intelligence components to solve them. Currently, there are a number of solu-
tions in which artificial intelligence components are effectively used in the coopera-
tive robotics industry, but one of the reasons for a large number of unresolved issues
in the industry (in particular, the problem of analyzing information in a cooperative
joint production environment) is the disparity of existing solutions. There are current-
ly a number of effective solutions with using the ontology in cooperative robotics as a
basis of the multifunctional cooperative robotics system, but they all belong to differ-
ent methodological approaches and are not integrated with each other, that is, there is
currently no multifunctional cooperative robotics system or technology based on the
ontological approach.
3 Structure of Multifunctional Cooperative Robotics System
based on the Ontological Approach
Cooperative using implies absolute safety, high functional flexibility and autonomy of
the used cobots. For this, the development of new systems and technologies in the
field of management, the creation of new solutions, the development of algorithms for
planning and execution of movements is necessary, that ensure the safety of physical
interaction between people and cobots.
Given that a simple scheme of two-tier tasks scheduling is not suitable for a coop-
erative joint production environment, then the multilayered scheme of a multifunc-
tional cooperative robotics system is important, which includes the tasks analysis,
tasks coordination, tasks performance, and task supervision. The requirement of ex-
change of information is also important since the robot in the team can only perceive
local information. Many tasks are currently too complex for robots and cobots, there-
fore coordination between people and robots must be ensured.
The cooperative joint production environment includes cobot(s), persons (collabo-
rators) and other components of production (including product). All components of
the cooperative joint production environment should communicate and share their
knowledge, and should process general information.
The multifunctional cooperative robotics system is a distributed intelligent system
in which the joint knowledge is used, the possibility of re-use of knowledge is real-
ized. The system consists of many rather independent modules (agents) that work
within this system and exchange knowledge with its other modules via messages. In
this system, cobots and persons work as a team in the cooperative joint production
environment. Each has its own tasks and responsibilities that require mutual coordina-
tion and communication.
The knowledge and information, which are exchanged by all components of the
cooperative robotics system, are provided in a natural language that must be under-
stood by both the cobots and the agents. Use of ontologies provides such an under-
standing of natural-language knowledge and information. Ontologies are the most
used structure for modelling the subject domains, for which cobots are used.
Structure of multifunctional cooperative robotics system based on ontological ap-
proach is represented on Figure 3.
Cooperative Joint Production Environment
Ontology-based intelligent agent (OBIA) for tasks analysis
Joint
Tasks, Messages knowledge Tasks, Messages
(ontology)
Ontology-based intelligent agent (OBIA) for tasks coordination
Joint
Tasks, Messages knowledge Tasks, Messages
(ontology)
Ontology-based intelligent agent (OBIA) for tasks performance
Joint
Tasks, Messages knowledge Tasks, Messages
(ontology)
Ontology-based intelligent agent (OBIA) for tasks supervision
Tasks, Tasks, Joint
Tasks, Tasks,
Messages Messages knowledge
Messages Messages
(ontology)
Set of Cobots Set of Persons
Cobot1 Cobot2 … CobotN Person1 Person2 … PersonM
Set of Products
Product1 Product2 … ProductK
Fig. 3. Structure of multifunctional cooperative robotics system based on ontological approach
The basis of the developed multifunctional cooperative robotics system based on on-
tological approach is the ontology-based intelligent agents (OBIAs) for tasks analysis,
tasks coordination, tasks performance and tasks supervision. OBIAs are the systems
situated within and are the parts of a cooperative joint environment that sense that envi-
ronment and act on it, over time, in pursuit of their own agenda and so as to effect what
it senses in the future. These OBIAs act, directing its activity towards achieving goals,
upon an environment using observation. OBIAs also learn and use joint knowledge to
achieve their goals. OBIAs accommodate new problem-solving rules incrementally,
adapt in real-time, learn and improve through interaction with the environment (with the
joint knowledge), analyze themselves in terms of behaviour, error and success. OBIAs
are used to autonomously gather information on a regular, programmed schedule or
when prompted by the user (cobot or person) in real-time. OBIAs are free to choose
between different actions. These agents seize the initiative to seek the best plan of action
to accomplish their assigned goals in light of the current situation and past experience
and then act on the cooperative joint production environment. The proposed OBIAs are
described by their environment (dynamic description), actions (change in the environ-
ment brought about by the agent), desires (overall policies or goals of the agent), action
selection architecture (the agent decides what to due next by consulting both its internal
state, the state of the world, and its current goal).
4 Results and Discussion
Let's consider the benefits of the developed multifunctional cooperative robotics system
based on the ontological approach. The developed system and the ontology-based intel-
ligent agents (OBIAs), that compose it, can handle complex tasks and "understand"
natural-language tasks and messages through the use of ontologies. Using ontologies for
the representation of the joint knowledge helps more rationally to plan and analyze the
information, which is received by each individual agent and cobot, and to make the
agents' and cobots' decisions more effective. The presented structure of the multifunc-
tional cooperative robotics system based on the ontological approach organizes easy
human intervention into the system because the persons enter to the proposed coopera-
tive joint production environment and have access to all the tasks, messages and com-
mon knowledge of the multifunctional cooperative robotics system. The use of ontolo-
gies in the structure of the developed multifunctional cooperative robotics system pro-
vides the formalization of "common sense" semantics, as well as speeding up and im-
proving the quality of processing the natural-language information.
Thus, the developed structure of the multifunctional cooperative robotics system
based on the ontological approach solves a number of now unresolved problems in the
field of cooperative robotics through the use of artificial intelligence components (in
particular, ontologies, which are currently the most effective structures for working with
natural-language texts and for formalizing the subject domains). So, the proposed multi-
functional cooperative robotics system is effective for the cooperative robotics industry
as it solves a number of difficult formalized tasks.
There is a huge amount of data coming daily to cobot through the production envi-
ronment. The problem is providing the safety and security of cobot and all the data in
a cooperation joint production environment in which people, cobots and computer
devices communicate and interact with one another.
5 Conclusions
In this paper, the structure of the multifunctional cooperative robotics system based
on the ontological approach is proposed. This system is based on ontology-based
intelligent agents (OBIAs), all joint knowledge of the system is represented as ontolo-
gies. The use of ontologies provides the following advantages for the developed sys-
tem: processing of complex tasks and "understanding" of natural-language tasks and
messages; planning and analysis of the information by each agent and the cobot; in-
creasing the effectiveness of agents' and cobots' decisions; formalization of the
"common sense" semantics; accelerating and improving the quality of processing
natural-language information. The developed structure of the multifunctional coopera-
tive robotics system based on the ontological approach considers both cobots and
humans as actants of the cooperative joint production environment, whereby they
have access to all the tasks, messages, and joint knowledge of the multifunctional
cooperative robotics system.
The future research of authors will devoted to: 1) developing the theoretical basis
for a smart production environment to ensure the safety and security of the cobot and
all data in a cooperative joint environment; 2) realization of the proposed multifunc-
tional cooperative robotics system based on ontological approach. Further work of the
authors will be aimed at solving these tasks.
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