AN ONTOLOGICAL APPROACH TO THE CREATION OF ROBOTIC COMPLEXES WITH AN INCREASED DEGREE OF AUTONOMY

  • S. М. Sokolov KIAM
Keywords: Mobile robotic complexes, information and control systems, increased degree of autonomy, knowledge representation, ontologies, interpretive navigation, information and motor actions

Abstract

The aspects necessary for the implementation of robotic complexes with an increased degree of
autonomy (RC with IDA) in practical work are considered. The distinctive features of such complexes,
the needs of the corresponding intelligent information control systems (IIСS) are indicated. The requirement
of situational awareness is highlighted and, as a consequence, the need for a diverse system
of knowledge representation, means of perception of the external environment and comparison of operational
information with models and a priori information about this environment. In addition, it is pointed
out the need to automate the processes of creating RC with IDA, accessibility, and simplification of their
use. In order to answer these questions, the paper proposes to use the concept and mechanisms of ontologies
in relation to autonomous robotics. Examples of existing solutions in this area are given. In robotics,
ontologies are used to define and conceptualize knowledge accepted by the community, using a
formal description that is machine-readable, shared, and contains the flexibility to justify this knowledge
in order to derive additional information. Ontologies are of considerable interest for multi-agent systems
for organizing interaction between agents and with other systems in heterogeneous environments,
the possibility of reuse and support for the development of new RCs. The author describes the construction
of an ontology proposed by the author in such an applied field as information support for targeted
movements of autonomous ground vehicles based on technical vision systems. All consideration is conducted
in the configuration space of the information and control systems of the RC with IDA. This space
allows you to aggregate a large number of different technologies used in the construction of RC. The
embodiment of a particular system in this space corresponds to the "assembly point". The coordination
of the forms of knowledge representation in the IICS is ensured by the consistent consideration of planes
in this space. As a connecting link – a means for automated translation of descriptions of descriptive
ontologies into descriptions of functional, machine-readable ontologies, the use of the language of information-
motor actions and interpretive navigation commands is proposed. In conclusion, the shortterm
prospects for the development of the described approach are considered, and wishes are expressed
to the domestic community of roboticists.

References

1. Christensen H.I., Sloman A., Kruijff G-J. & J. Wyatt (Eds.) Cognitive Systems. Reports on the
European Union project on Cognitive Systems. Available at: https://cordis.europa.eu/project/.
2. RSS novosti. Novosti iskusstvennogo intellekta [RSS news. Artificial Intelligence News].
Available at: https://ai-news.ru/2021/01/bespilotnye_karernye_samosvaly.html.
3. OOO «MosTransArenda» [MosTransArenda LLC]. Available at: https://mtarenda.ru/articles/
trend-na-bespilotnye-samosvaly-nabiraet-oboroty/#.
4. KOMEK MASHINERI. Doydut li bespilotnye samosvaly Komatsu do Rossii? Ischeznet li
professiya voditelya? [KOMEK MACHINERY. Will Komatsu unmanned dump trucks reach
Russia? Will the driver's profession disappear?]. Available at: https://www.komek.ru/
staty/doydut-li-bespilotnye-samosvaly-komatsu-do-rossii-ischeznet-li-professiya-voditelya/.
5. Tadviser. Produkt:BelAZ-7513R_(bespilotnyy_samosval) [Advisor. Product: BelAZ-7513 R_
(unmanned dump truck). Available at: https://www.tadviser.ru/index.php|/Produkt:BelAZ-
7513R_(bespilotnyy_samosval).
6. Andrey Kozhemyakin. Samosval na million [Dump truck for a million]. Available at:
https://dev.by/news/samosval-na-million.
7. Petrichkovich Ya., Solokhina T., i dr. RoboDeus – 50-yadernaya geterogennaya SnK dlya
vstraivaemykh sistem i robototekhniki [RoboDeus - 50-nuclear heterogeneous SnC for embedded
systems and robotics], Elektronika [Electronics], 2020, No. 7 (00198), pp. 52-63.
8. NVidia Jetson AGX Xavier. Available at: https://www.nvidia.com/ru-ru/autonomousmachines/
embedded-systems/jetson-agx-xavier/.
9. Accelerate Automotive with Intel. Available at: https://intel.ru/content/www/ru/ru/ automotive/
products/programmable/overview.html.
10. Stephen Shankland. Tesla self-driving car computer. Available at: https://www.cnet.com/news/
meet-tesla-self-driving-car-computer-and-its-two-ai-brains.
11. Genesereth M. and Fikes R. Knowledge Interchange Format, Stanford Logic Report Logic-92-
1. Stanford University, 1992.
12. CommonKADS General Information. Available at: http://www.commonkads.uva.nl/framesetcommonkads.
html, 2003.
13. Kiril'chenko A.A., Platonov A.K., Sokolov S.M. Teoreticheskie aspekty organizatsii
interpretiruyushchey navigatsii mobil'nogo robota. Preprint IPM № 19 [Theoretical aspects of the
organization of interpretive navigation of a mobile robot. IPM Preprint No. 19]. Moscow, 2008.
14. Laird J.E., Newell A. and Rosenbloom P.S. Soar: An Architectue for General Intelligence,
Artificial Intelligence, 1987, Vol. 33, pp. 1-64.
15. Newell A. and Simon H. GPS; A Program that Simulates Human Thought, McGraw-Hill, 1963.
16. Pearson J.D., Huffman S.B., Willis M.B., Laird J.E. and Jones R.M. A Symbolic Solution to
Intelligent Real-Time Control, Robotics and Autonomous Systems, 1993, No. 11, pp. 279-291.
17. Etherington D. What Does Knowledge Representation Have to Say to Artificial Intelligence?
Proceesings ate the AAAI, 1997.
18. Siegwart R., Nourbakhsh I. Introduction to Autonomous Mobile Robots. MIT Press 1997.
19. Volpe R., Estlin T., Laubach S., Olson C. and Balaram J. Enhanced Mars Rover Navigation
Techniques, Proceedings of the IEEE International Conference on Robotics and Automation,
San Francisco, CA, 2000.
20. Wasson G., Kortenkamp D. and Huber E. Integrating Active Perception with an Autonomous
Robot Architecture, Robotics and Automation Journal, 1999, Vol. 29, pp. 175-186.
21. Brooks R.A. A Robust Layered Control System for a Mobile Robot, MIT AI Lab, A.I. Memo
864, Sept. 1985.
22. Maes P. and Brooks R. Learning to Coordinate Behaviors, Proceedings AAAI, 1990, pp. 796-802
23. Albus J., Lumia R., Fiala J. and Wavering A. NASREM: The NASA/NBS Standard Reference
Model for Telerobot Control System Architecture, Proceedings of the 20th International Symposium
on Industrial Robots, Tokyo, Japan, 1989.
24. Arkin R. Navigational Path Planning for a Vision-Based Mobile Robot, Robotica, 2003, No. 7,
pp. 49-63.
25. Thorpe C. Vision and Navigation for the Carnegie Mellow NavLab, IEEE PAMI, 1988, No. 10 (3).
26. Kuipers B. The Spatial Semantic Hierarchy, Artificial Intelligence, 2000, Vol. 119 (1–2), pp.
191-233.
27. Maimone M. A Martian Vision. JPL, 2016.
28. Delaune J. Vision-Based Navigation for Mars Helicopters. JPL, 2021.
29. Dickmanns E.D. A General Dynamic Vision Architecture for UGV and UAV, Journal of Applied
Intelligence, 1992, No. 2, pp. 251.
30. Lauria S., Kyriacou T., Bugmann G., Bos J., Klein E. Converting natural language route instructions
into robot executable procedures, Proc. IEEE Int. Workshop Roman, Berlin, Germany,
2002, pp. 223-228.
31. Parker L.E. ALLIANCE: An architecture for fault tolerant multirobot cooperation, Robotics
and Automation, IEEE Transactions on, 1998, Vol. 14, No. 2, pp. 220240.
32. Sellami Z., Camps V., Aussenac-Gilles N., and Rougemaille S. Ontology Co-construction with
an Adaptive Multi-Agent System: Principles and Case-Study, Knowledge Discovery,
Knowlege Engineering and Knowledge Management, 2011, pp. 237248-
33. Schlenoff C. and Messina E. A robot ontology for urban search and rescue, in Proceedings of
the 2005 ACM workshop on Research in knowledge representation for autonomous systems,
2005, pp. 2734.
34. Tran Q. and Low G. MOBMAS: A methodology for ontology based multi-agent systems development,
Information and Software Technology, Jun. 2008, Vol. 50, No. 7-8, pp. 697-722.
35. Gennari J.H., Musen M.A., Fergerson R.W., Grosso W.E., CrubEzy M., Eriksson H., Noy N.F.,
and ` Tu S.W. The evolution of prot`eg`e: an environment for knowledge-based systems development,
International Journal of Human-Computer Studies, 2003, Vol. 58 (1), pp. 89-123,
36. Matthews B. Semantic web technologies, E-learning, 2005, No. 6(6):8.
37. Pearson J.D., Huffman S.B., Willis M.B., Laird J.E. and Jones R.M. A Symbolic Solution to
Intelligent Real-Time Control, Robotics and Autonomous Systems, 1993, No. 11, pp. 279-291.
38. Schlenoff C. Linking Sensed Images to an Ontology of Obstacles to Aid in Autonomous Driving,
Proceedings of the 18th National Conference on Artificial Intelligence: Workshop on Ontologies
for the Semantic Web, 2002.
39. Wasson G., Kortenkamp D. and Huber E. Integrating Active Perception with an Autonomous
Robot Architecture, Robotics and Automation Journal, 1999, Vol. 29, pp. 175-186.
40. Chella A., Cossentino M., Pirrone R., and Ruisi A. Modeling ontologies for robotic environments,
In pr oceedings of the 14th international conference on Software engineering and
knowledge engineering. ACM, 2002, P. 80.
41. Mendoza R., Johnston B., Yang F., Huang Z., Chen X., and Williams M. OBOC: Ontology
Based Object Categorisation for Robots, In Proceedings of the 4th International Conference
on Computational Intelligence, Robotics and Automation (CIRAS 2007), Palmerston North,
New Zealand. Citeseer, 2007.
42. Molich R. and Nielsen J. Improving a human-computer dialogue, Communications of the
ACM, 1990, Vol. 33 (3), pp. 348.
43. Yanco H. and Drury J. Classifying human-robot interaction: an updated taxonomy, In Systems,
Man and Cybernetics, 2004 IEEE International Conference on, 2004, Vol. 3, pp. 2841-2846.
44. Juarez A., Bartneck C., & Feijs L. Using Sematic Web Technologies to Desribe Robotic Embodiments,
Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction,
Lausanne, 2011, pp. 425-432.
45. Boguslavskiy A.A. i dr. Modeli i algoritmy dlya intellektual'nykh sistem upravleniya [Models
and algorithms for intelligent control systems]. Moscow: IPM im. M.V. Keldysha RAN, 2019,
232 p.
46. Akhterov A.V. Nekotorye aspekty interpretiruyushchey navigatsii mobil'nogo robota. Preprint
IPM № 97 [Some aspects of the mobile robot's interpretive navigation. IPM preprint No. 97].
Moscow: 2005, 16 p.
47. Boguslavsky A.A., Sokolov S.M. Component Approach to the Applied Visual System Software
Development, 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI
2003), July 27-30, Orlando, Florida, USA, 2003.
48. Newman W. A Systematic Approach to Learning Robot Programming with ROS. CRC Press,
2018.
Published
2022-04-20
Section
SECTION I. PROSPECTS FOR THE USE OF ROBOTIC SYSTEMS