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Issue 2 (216), article 5

DOI:

Cybernetics and Computer Engineering, 2024,2(216)

Shepetukha Yu.M., PhD (Engineering), Senior Researcher
Leading Researcher of the Intelligent Control Department
https://orcid.org/0000-0002-6256-5248
e-mail: yshep@meta.ua

Bondar S.O., PhD (Engineering),
Head of the Intelligent Control Department,
https://orcid.org/0000-0003-4140-7985
e-mail: seriybrm@gmail.com

Hubsky Ya.M., PhD Student,
https://orcid.org/0009-0009-4484-9544
e-mail: s.gubsky@gmail.com

Popov I.V., PhD Student,
Junior Researcher of the Intelligent Control Department
https://orcid.org/0009-0009-7961-9431
e-mail: popigor7@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Science
and Ministry of Education and Science of Ukraine
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

METHODS OF INTELLECTUALISATION OF SPATIAL SCENE MONITORING PROCESSES

Introduction. The development of intelligent technologies requires the active use of advanced technologies and innovative approaches for the intellectualization of spatial scene monitoring processes. The relevance of the topic lies in the great need to improve the quality of video content production. In particular, there is interest in the automation and further intellectualization of shooting processes. The use of new methods of intellectualization leads to a reduction of permissible errors when creating a creative video project. Intellectualization of data processing processes from markers, namely the use of artificial intelligence (AI) methods, allows to obtain a controlled level of quality with minimal human involvement. Intellectualization of stage production contributes to the creation of exciting and innovative performances that captivate the audience. It allows creating new ways of interacting with the audience and providing them with unique impressions from cultural events.

The purpose  of the paper is to study the methods of intellectualization of data processing from markers during the use of automatic video cameras in tasks of observing stage action for video-photography.

The results. The issue of the interaction of markers with cameras in three-dimensional space, which is completely identical to the built 3D model, is considered.

Conclusions. The information technology of spatial monitoring of the scene can increase the efficiency and simplify the use of automatic video cameras in the tasks of monitoring the stage action for video-photo shooting. There is no one universal “best” method, as each algorithm has its own advantages and disadvantages. However, the optical flow gradient calculation method may be considered more suitable for use in stage production.

The introduction of information technology for spatial scene monitoring based on the optical flow gradient calculation method will improve efficiency and simplify the use of automatic video cameras. The use of surveillance information technology will reduce the burden on the personnel who maintain and manage the filming and are involved in the work.

Keywords: intellectualization of data processing processes, intelligent monitoring, automatic video camera, animation, optical flow gradient, computer vision.

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REFERENCES

  1. O.Ye. Volkov, Yu.M. Shepetukha, Yu.P. Bogachuk, M.M. Komar, D.O. Volosheniuk. Experience in development and implementation of intelligent systems for control of dynamic objects. Control Systems and Computers, 2022, Issue 1 (297), pp. 64-81
  2. Stephen Hughes, Michael Lewis. Robotic camera control for remote exploration. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 2004, pp. 511–517
  3. Lefevre F., Bombardier V., Charpentier P. Context-based camera selection from multiple video streams. Multimed Tools Appl. 81, pp. 2803–2826 (2022).
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  5. N. Dagnes, K. Ben-Mansour, F. Marcolin, F. Marin, F.R. Sarhan, S. Dakpé, E. Vezzetti. What is the best set of markers for facial movements recognition. Annals of Physical and Rehabilitation Medicine. 2018,
  6. M. Gustavo, Steadicam shot. The Filmmaker’s Eye, 2022, pp.197-202
  7. Griffith D.A. Spatial Filtering. In: Fischer, M., Getis, A. (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg, 2010.
  8. Simsek E., Ozyer B. Selected Three Frame Difference Method for Moving Object Detection. International Journal of Intelligent Systems and Applications in Engineering. 2021, 9(2), pp.48–54.

Received 06.03.2024

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Issue 2 (216), article 4

DOI:

Cybernetics and Computer Engineering, 2024,2(216)

Gladun A.Ya., PhD (Engineering),
Senior Researcher of the Department of Complex Research
of Information Technologies and Systems
https://orcid.org/0000-0002-4133-8169,
e-mail: glanat@yahoo.com

Khala K.O.,
Researcher of the Department of Complex Research
of Information Technologies and Systems
https://orcid.org/0000-0002-9477-970X,
e-mail: cecerongreat@ukr.net

International Research and Training Center
for Information Technologies and Systems of the National Academy of Sciences
of Ukraine and Ministry of Education and Science of Ukraine
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

ONTOLOGY-ORIENTED MULTI-AGENT SYSTEM FOR DECENTRALIZED CONTROL OF UAV’S GROUP

Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI), and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group.

The purpose of the paper is to develop the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAC architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description language, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. It is needed to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed.

Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources.

Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods are based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes.

Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs is proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence is suggested. This system is based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which are based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems.

Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation

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Received 04.03.2024

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Issue 2 (216), article 3

DOI:

Cybernetics and Computer Engineering, 2024,2(216)

Volosheniuk D.O., PhD (Engineering), Senior Researcher
Head of the Research Laboratory of Unmanned Complexes and Systems
https://orcid.org/0000-0003-3793-7801,
e-mail: p-h-o-e-n-i-x@ukr.net

Tymchyshyn R.M., PhD Student,
Researcher of the Research Laboratory of Unmanned Complexes and Systems
https://orcid.org/0000-0002-4243-4240,
e-mail: romantymchyshyn.rt@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Science
and Ministry of Education and Science of Ukraine
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

EDGE DETECTION ALGORITHM FOR MONITORING OF TRANSPORT INFRASTRUCTURE

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool.

Purpose. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results.

Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images.

The goal of this paper is to present an algorithm crafted specifically for the task of monitoring transport infrastructure.

Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms.

Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. An algorithm for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for working in the domain of transport infrastructure monitoring. The algorithm leverages the narrow specialization of the task to improve the obtained results. The foundation of the algorithm is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms.

Conclusion. The proposed algorithm can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data.

Keywords. Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.

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REFERENCES

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Received 29.04.2024

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Issue 2 (216), article 2

DOI:

Cybernetics and Computer Engineering, 2024,2(216)

Smirnov A.O., PhD Student,
https://orcid.org/0009-0002-6509-4135,
e-mail: tonysmn97@gmail.com

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Science of Ukraine
and the Ministry of Education and Science of Ukraine.
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

CAMERA POSE ESTIMATION USING A 3D GAUSSIAN SPLATTING RADIANCE FIELD

Introduction. Accurate camera pose estimation is crucial for many applications ranging from robotics to virtual and augmented reality. The process of determining agents pose from a set of observations is called odometry. This work focuses on visual odometry, which utilizes only images from camera as the input data.

The purpose of the paper is to demonstrate an approach for small-scale camera pose estimation using 3D Gaussians as the environment representation. 

Methods. Given the rise of neural volumetric representations for the environment reconstruction, this work relies on Gaussian Splatting algorithm for high-fidelity volumetric representation.

Results. For a trained Gaussian Splatting model and the target image, unseen during training, we estimate its camera pose using differentiable rendering and gradient-based optimization methods. Gradients with respect to camera pose are computed directly from image-space per-pixel loss function via backpropagation. 

The choice of Gaussian Splatting as representation is particularly appealing because it allows for end-to-end estimation and removes several stages that are common for more classical algorithms. And differentiable rasterization as the image formation algorithm provides real-time performance which facilitates its use in real-world applications.

Conclusions. This end-to-end approach greatly simplifies camera pose estimation, avoiding compounding errors that are common for multi-stage algorithms and provides a high-quality camera pose estimation. 

Keywords: radiance fields, scientific computing, odometry, slam, pose estimation, gaussian splatting, differentiable rendering 

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2. Bernhard Kerbl. 3D Gaussian Splatting for Real-Time Radiance Field Rendering, 2023, arXiv: 2308.04079 [cs.GR].

3. Kai Li Lim, Thomas Braunl. A Review of Visual Odometry Methods and Its Applications for Autonomous Driving, 2020, arXiv: 2009 . 09193 [cs.CV].

4. Tony Lindeberg. Scale Invariant Feature Transform. Vol. 7, May 2012,

doi: 10.4249/scholarpedia.10491.

5. Ben Mildenhall. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, 2020, arXiv: 2003.08934 [cs.CV].

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Received 29.03.2024

admin

Issue 2 (216), article 1

DOI:

Cybernetics and Computer Engineering, 2024,2(216)

Volkov O.Ye., PhD (Engineering), Senior Researcher,
Director
https://orcid.org/0000-0002-5418-6723,
e-mail: alexvolk@ukr.net

Dzhebrailov R.Yu., PhD Student,
Junior Researcher of the Research Laboratory of Unmanned Complexes and Systems,
https://orcid.org/0000-0002-4473-9670,
e-mail: rombik1197@gmail.com

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Science of Ukraine
and the Ministry of Education and Science of Ukraine.
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

TESTING THE METHOD OF TOPOGRAPHIC AFFINITY OF IMAGES ON IMAGES OF THE EARTH’S SURFACE

Introduction. In connection with the development of the method of topographic affinity of images, it became necessary to conduct its testing according to the criteria of workability and efficiency.

The purpose of the paper is to test the method of determining the topographic affinity of images based on taking into account the detected special zones on the images of the natural landscape for the autonomous navigation of UAVs.

Results. According to the results of testing for three tasks, the method showed its effectiveness at the level of 100%.

Conclusions. The method of topographic affinity of images can work with a large number of complex and diverse images of the Earth’s surface, which cannot be analyzed by other known methods, and with high efficiency. It can be used to build a system of autonomous navigation of UAVs separately or together with other methods.

Keywords: unmanned aerial vehicle, unmanned aircraft complex, autonomous navigation, special points, special areas, method of analysis of special areas of images.

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REFERENCES

1. Irani G., Christ J. Image processing for Tomahawk scene matching. Johns Hopkins APL Technical Digest. 1994, No, 3, pp. 250–264.

2. Ronghao Li, Pengqi Gao, Xiangyuan Cai, Xiaotong Chen, Jiangnan Wei, Yinqian Cheng, Hongying Zhao. A Real-Time Incremental Video Mosaic Framework for UAV. Remote Sens. 2023, No, 15, pp. 250–264.

3. Autonomous navigation system for unmanned aerial vehicle based on topographic clustering of visual images : pat. UA 121833 C2 Ukraine: 2020.01. № a 2019 05904 ; application for application filed 29.05.2019 ; published 27.07.2020, Bulletin No. 14. 18 p. [In Ukrainian]

4. Dzhebrailov R.Yu.,  Gospodarchuk O.Yu. Detection of Special Zones as a Basis for the Method of Topographic Affinity of Images Cybernetics and Computer Engineering, 2024, № 1, pp. 20–34.  https://doi.org/10.15407/kvt215.01.020 [In Ukrainian]

Received 23.03.2024

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Issue 2 (216)

DOI:

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TABLE OF CONTENTS:

Informatics and Information Technologies:

Volkov O.Ye., Dzhebrailov R.Yu.
Testing the Method of Topographic Affinity of Images on Images of the Earth’s Surface

Smirnov A.O.
Camera Pose Estimation Using a 3D Gaussian Splatting Radiance Field

Volosheniuk D.O., Tymchyshyn R.M.
Edge Detection Algorithm for Monitoring of Transport Infrastructure

Intelligent Control and Systems:

Gladun A.Ya., Khala K.O.
Ontology-Oriented Multy-Agent System for Decentralized Control of UAV’s Group

Shepetukha Yu. M., Bondar S.O., Hubsky Ya.M., Popov I.V.
Methods of Intellectualisation of Spatial Scene Monitoring Processes

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Issue 1 (215), article 5

DOI:https://doi.org/10.15407/kvt215.01.067

Cybernetics and Computer Engineering, 2024,1(215)

Shepetukha Y.M., PhD (Technics), Senior Researcher,
Leading Researcher of Intelligent Control Department
https://orcid.org/0000-0002-6256-5248
e-mail: yshep@meta.ua

Semenog R.V., PhD Student,
Deputy Head of the Research Laboratory of Unmanned Complexes and Systems
https://orcid.org/0000-0002-6714-0644
e-mail: ruslansemenog20@icloud.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

SEQUENTIAL STRUCTURING METHOD FOR BUILDING DYNAMIC OBJECTS MANAGEMENT SYSTEMS

Introduction. The growing role of information technologies makes them an important part of modern facilities management systems and contributes to increasing their efficiency, security and their ability to adapt to changes. A new type of information systems is emerging that uses advanced technologies to automate and optimize management processes, including artificial intelligence and others — Intelligent Management Systems(IMS), is emerging now.

The next step in this direction is complex systems that allow dynamic objects to be controlled independently of external intervention — autonomous control systems (ACS). SACs use a variety of sensors, data processing and decision-making algorithms and are widely used in the automotive industry (for example, self-driving cars), in unmanned aerial vehicles (drones), and many other industries where independent and efficient control of objects is required.

The purpose of the article is to investigate modern concepts of building autonomous control systems for dynamic objects and to describe methods of intellectualization of such systems.

The results. A modern approach to the construction of systems of autonomous control of dynamic objects, based on sequential structuring, was studied. Methods of creating systems aimed at optimizing automatic management of dynamic objects are highlighted.

Conclusions. A promising direction of research is the development of a new generation of intelligent information technologies that use information processing mechanisms that are based on the method of sequential structuring in the construction of automatic control systems.

Concepts for building automatic control systems should ensure the application of meaningful data processing methods and use components of synergistic interaction of human-machine control systems. The application of the methodology of sequential structuring of weakly formalized components of intellectual problems in visual management systems allows achieving some unification in solving a certain class of intellectual management problems.

The further direction of research consists in the development of a new generation of information technologies and the corresponding toolkit of automatic control, which will apply methods of meaningful data processing, in particular, the method of sequential structuring for the intellectualization of automatic control systems.

Keywords: intelligent information technology, artificial intelligence, intelligent control, dynamic object, imaginative thinking, autonomy.

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REFERENCES

1. Imam I. F., Kondratoff Y. Intelligent adaptive agents: a highlight of the AAAI-96 workshop. AI Mag. 1997, Vol. 18, No. 3, pp. 75-80.

2. Pigozzi G., Tsoukias A., Viappiani P. Preferences in artificial intelligence. Annals Math. and Artif. Intell. 2016, Vol. 77, No. 3/4, pp. 361-401.
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4. Revesz P.Z. Artificial intelligence basic research directions at the U.S. Air Force. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, №6. Access mode: http://cse.unl.edu/~revesz/papers/IJAIT14.pdf. – name from the screen.
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5. V.L. Deepak, S. Nayak, S. Patra. Development of obstacle-avoiding robots using RF technology. Intern. J. Intell. Unmanned Syst. 2016, Vol. 4, No. 4, pp. 214-225.
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6. Hanratty T.P., Newcomb E.A., Hammell R.J. A fuzzy-based approach to support decision making in complex military environments. Intern. J. Intell. Inform. Technol. 2016, Vol. 12, No. 1, pp. 1-30.
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7. Mukhlish F., Page J., Bain M. Evolutionary-learning framework: improving automatic swarm robotics design. Intern. J. Intell. Unmanned Syst. 2018, Vol. 6, No. 4, pp. 197-215.
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8. Walczak S. Society of agents: a framework for multi-agent collaborative problem solving. Intern. J. Intell. Inform. Technol. 2018, Vol. 14, No. 4, pp. 1-23.
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9. Groumpos P. P. Complex systems and intelligent control: Issues and challenges. Large Scale Systems: Theory and Applications 2001 : 9th IFAC symp. : 18-20 July, 2001, Bucharest, Romania, pp. 29-36.
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10. Gonzales D., Harting S. Designing unmanned systems with greater autonomy. RAND Corporation, Santa Monica, CA, USA. 2014. Access mode: https://www.rand.org/content/ dam/rand/pubs/research_reports/RR600/RR626/RAND_RR626.pdf

11. Mostafa S.A., Ahmad M.S., Mustapha A. Adjustable autonomy: a systematic literature review. Artificial Intelligence Review. 2019, Vol. 51, №3, pp. 149-186.
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14. Shepetukha Yu.M., Volkov O.Ye., Komar M.M.. Intellectualization of decision making processes in autonomous control systems. Cybernetics and computer engineering. 2021, N. 2 (204), pp. 49-63 http://jnas.nbuv.gov.ua/article/UJRN-0001262408
https://doi.org/10.15407/kvt204.02.049

Received 04.01.2024

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Issue 1 (215), article 4

DOI:https://doi.org/10.15407/kvt215.01.048

Cybernetics and Computer Engineering, 2024,1(215)

Volosheniuk D.O., PhD (Technics), Senior Researcher,
Head of the Research Laboratory of Unmanned Complexes and System
https://orcid.org/0000-0003-3793-7801,
e-mail: p-h-o-e-n-i-x@ukr.net

Tymchyshyn R.M., PhD Student,
Researcher of the Research Laboratory of Unmanned Complexes and System
https://orcid.org/0000-0002-4243-4240,
e-mail: romantymchyshyn.rt@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

INTELLIGENT INFORMATION TECHNOLOGY
FOR TRANSPORT INFRASTRUCTURE MONITORING

Introduction. The rapid growth in the number of UAV developments in recent years is associated with serious technological advances in various fields. In particular, successes in the field of geographic information systems (GIS) raise the problem of dynamic modeling of reality and maintenance of cartographic information in an up-to-date state. To eliminate it, it is necessary to solve the problem of prompt introduction of changes. If the information contained in the GIS is inaccurate or outdated, its analysis will not correspond to the real situation, which can lead to wrong decisions.

That is why data about objects in GIS need systematic updating. Without updating the GIS data, they cannot perform the tasks for which they are intended. Therefore, the joint use of UAVs and GIS becomes an important component of a unified information and communication environment.

The purpose of the article is to investigate the information technology of ensuring the process of monitoring the transport infrastructure using the optical systems of an unmanned aerial vehicle (UAV).

The results. The issue of the need to create a new perspective system for controlling unmanned aerial vehicles and the development of methods for its intellectualization was considered. The ideas of applying the theory of invariance and autonomy for the synthesis of promising control systems are proposed, as well as a number of methods for ensuring a high level of their intellectualization. An approach to solving the problem is proposed, based on the theory of high-precision remote control of dynamic objects, as well as on the complex interaction of the methods of the theory of autonomy, adaptive control, and intellectualization of the processes of control of dynamic objects.

Conclusions. The obtained research results indicate the possibility of successful application of the developed transport infrastructure monitoring technology, in particular for the management of engineering networks. The solutions used in the developed technology are universal and capable of solving complex tasks for electric, water, heat and gas networks, as well as for roads and railways.

Prospects for further research are to improve the quality of thematic processing methods to improve the classification result. The developed methods can be used instead of expert decryption of monitoring data with similar and even higher accuracy.

Keywords: unmanned aerial vehicle, control system, invariance, intellectualization, autonomy.

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REFERENCES

1. Modern information technologies in the tasks of navigation and guidance of unmanned maneuverable aircraft / Edited by: M.N. Krasylshchikova, H.G. Serebryakova M.: FIZMATLYT, 2009. 556 p.

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Received 03.01.2024

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Issue 1 (215), article 3

DOI:https://doi.org/10.15407/kvt215.01.035

Cybernetics and Computer Engineering, 2024,1(215)

Komar M.M., PhD (Engineering), Senior Researcher 
Deputy Director for Scientific and Organizational Work,
https://orcid.org/0000-0001-9194-2850,
e-mail:nickkomar08@gmail.com

Chepizhenko V.I., DSc (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department,
https://orcid.org/0000-0001-8797-4868,
e-mail: chepizhenko.valeriy@gmail.com

Bogachuk Yu.P., PhD (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department
https://orcid.org/0000-0002-3663-350X,
e-mail: dep185@irtc.org.ua

Soloviev M.V., PhD Student,
Leading Engineer of the Research Laboratory of Unmanned Complexes and Systems
https://orcid.org/0009-0003-5131-7497,
e-mail: 19Leviathan90@gmail.com

International Research and Training Center for Information
Technologies and Systems of the National Academy of Sciences
of Ukraine and the Ministry of Education and Science of Ukraine,
40, Akad. Glushkov av., Kyiv, 03187, Ukraine

DEVELOPMENT OF THE MULTI PURPOSE SIMULATION COMPLEX
FOR TRAINING OF UNMANNED SYSTEMS OPERATORS

Introduction. This paper discusses the development of a multi-domain simulation complex for training unmanned systems operators. Active use of unmanned systems, their improvement, and complication of designs require the creation and development of simulators and modeling equipment, the use of which ensures effective training of competent operators.

The purpose of the paper is development of the multipurpose simulation complex for training of unmanned systems operators and for performing experimental and research works.

The methods. The following methods were used during the work: methods of automatic control, theory of navigation, theory of group decision making, theory of construction of distributed control systems for aircraft in a network-centric environment, methods of semi-natural modeling, methods of software engineering, methods for evaluating the piloting characteristics and stability and controllability characteristics of simulators, methods for evaluating the visualization systems of simulators.

The results. As a result of the work, a prototype of the complex was created, which can be used for the development and research of aircraft control systems, training of unmanned systems  operators, and conducting experimental research.

Conclusions. The developed prototype of the multi-domain simulation complex is a tool for solving the problem of quality training of operators. The complex allows operators to be trained in a safe environment, which reduces the risk of equipment damage and injuries to people. In addition, the complex allows for the study of aircraft control systems and the development of new control algorithms.

Keywords: Unmanned system, Training complex, Virtual environment, Operator, Simulation, Simulation complex, Control systems.

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27. Volkov O., Komar M., Synytsya K., & Volosheniuk D. The UAV simulation complex for operator training. Multi Conference on Computer Science and Information Systems, MCCSIS 2019-Proceedings of the International Conference on e-Learning 2019. 2019, pp. 313-316.
https://doi.org/10.33965/el2019_201909R044

Received 29.12.2023

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Issue 1 (215), article 2

DOI:https://doi.org/10.15407/kvt215.01.020

Cybernetics and Computer Engineering, 2024,1(215)

Dzhebrailov R.Yu., PhD Student,
Junior Researcher of the Research Laboratory of Unmanned Complexes and Systems,
https://orcid.org/0000-0002-4473-9670,
e-mail: rombik1197@gmail.com

Gospodarchuk O.Yu.,
Senior Researcher of the Intelligent Control Department
https://orcid.org/0000-0001-6619-2277,
e-mail: olexago@gmail.com

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Science of Ukraine
and the Ministry of Education and Science of Ukraine.
40, Acad. Glushkov av., 03187, Kyiv, Ukraine

DETECTION OF SPECIAL ZONES AS A BASIS FOR THE METHOD OF TOPOGRAPHIC AFFINITY OF IMAGES

Introduction. The satellite and inertial navigation systems of an unmanned aerial vehicle (UAV) or unmanned aircraft system (UAS) have their drawbacks. Attempts to eliminate these shortcomings are to develop an autonomous navigation system. The officially patented model of an autonomous navigation system, as it turned out, also has its drawbacks. Accordingly, there is a need to improve such an autonomous navigation system.

The purpose of the paper is to develop and study a method for determining the topographic affinity of images based on the detected special zones in images of the natural landscape for autonomous UAV navigation.

Results. A method of topographic affinity of visual images based on the detection of special zones by searching for local maxima of the Laplace operator in the image has been developed. The method of topographic affinity of images allows  involving a smaller number of special points for comparison, which reduces the amount of required memory resources and increases performance.

Conclusions. The proposed method of topographic affinity of images based on the detection of special zones (blob detection methods) based on the principle of searching for local maxima of the Laplace operator can be used to build an autonomous navigation system for UAVs. The algorithmic implementation of the method has shown that it can work with a large number of complex and diverse images of the earth’s surface obtained during UAV flights, is effective by increasing the processing speed of the studied images, and can be implemented to create full-fledged UAV autonomous navigation systems.

Keywords: unmanned aerial vehicle, unmanned aerial vehicle complex, autonomous navigation, special points, special zones, method of special image zones analysis.

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https://doi.org/10.1109/CarpathianCC.2012.6228752

Received 19.12.2023