Intellectual Control, Localization and Mapping in Geographic Information Systems Based on Analysis of Visual Data

Gritsenko, V.; Волков, Олександр Вікторович; Bogachuk, Yuriy P.; Gospodarchuk, Oleksiy Yu.; Komar, Myroslav; Shepetukha, Y.; Volosheniuk, D.

doi:10.15407/kvt200.02.041

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Аналіз отриманих за допомогою цих пристроїв даних дає змогу реалізовувати наступні інтелектуальні функції: розпізнавання об'єктів за допомогою методів комп'ютерного бачення; розпізнавання об'єктів за акустичними сигнатурами; адаптація до змін зовнішніх умов, параметрів та структури системи керування. У цьому напрямі досліджень було розроблено та запатентовано систему оптико-акустичної пеленгації, що забезпечує сталість захисту контрольованого повітряного простору [17]. Особливістю розробленої системи є використання взаємоузгодженого розподіленого акустичного та оптичного стеження за потенційно небезпечним об'єктом, що підвищує надійність його виявлення, пеленгації, ідентифікації та класифікації.…”

Section: забезпечення сталого керування за умов суттєвих інформаційни...unclassified

Experience in Development and Implementation of Intelligent Systems for Control of Dynamic Objects

Volkov¹,

Shepetukha²,

Bogachuk³

et al. 2022

Control syst. comput.

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Introduction. An important area of application of advanced information technologies is intelligent control of dynamic objects in complex environment. The purpose of the article is to analyze and generalize theoretical as well as practical results related to elaboration of systems for intelligent control of dynamic objects. Future research prospects in this area are also outlined. Methods. Methodological tools include theories of intelligent control, decision making, artificial intelligence, computer vision, system analysis, acoustics, problem solving, conflict resolution. Results. The article outlines main results related to intelligent control of dynamic objects in complex and changing environment. Issues of system integrity, distinctions between traditional and intelligent control, usage of artificial intelligence in autonomous systems, data structuring, image comprehension of current situation have been discussed. Information about the implementation of high-precision control systems for various kinds of dynamic objects (sea vessels, aircrafts, UAVs) in the conditions of conflicts as well as essential informational restrictions has been presented. Though the article covers a wide spectrum of research activities with a considerable period, the authors tried to stress both interconnections and succession of scientific as well as technological achievements in the examined area. Conclusions. Main directions of future research include development and implementation of new approaches to the increase of intellectualization level for information technologies and systems.

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Section: забезпечення сталого керування за умов суттєвих інформаційни...unclassified

Experience in Development and Implementation of Intelligent Systems for Control of Dynamic Objects

Volkov¹,

Shepetukha²,

Bogachuk³

et al. 2022

Control syst. comput.

View full text Add to dashboard Cite

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“…This information could be obtained by remote sensing, mapping, and various types of aerial imagery. Such tasks include operational tasks that are solved through the use of UAVs, for example, for dynamic monitoring (evaluation and anal-ysis) of the state of transport infrastructure (road surface quality) [5]. The main feature of operational databases of geoinformation systems is the ability to represent data in the form of spatial-time layers of flight mapping images.…”

Section: Introductionmentioning

confidence: 99%

Devising an image processing method for transport infrastructure monitoring systems

Volkov

Komar

Volosheniuk

2021

EEJET

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Identifying and categorizing contours in images is important in many areas of computer vision. Examples include such operational tasks solved by using unmanned aerial vehicles as dynamic monitoring of the condition of transport infrastructure, in particular road markings. This study has established that current methods of image contour analysis do not produce clear and reliable results when solving the task of monitoring the state of road markings. Therefore, it is a relevant scientific and applied task to improve the methods and models of filtration, processing of binary images, and qualitative and meaningful separation of the boundaries of objects of interest. To solve the task of highlighting road marking contours on images acquired from an unmanned aerial vehicle, a method has been devised that includes an operational tool for image preprocessing – a combined filter. The method has several advantages and eliminates the limitations of known methods in determining the boundaries of the location of the object of interest, by highlighting the contours of a cluster of points using histograms. The method and procedures reported here make it possible to successfully solve problems that are largely similar to those that an expert person can face when solving intelligent tasks of processing and filtering information. The proposed method was verified at an enterprise producing the Ukrainian unmanned aerial vehicle "Spectator" during tests of information technology of dynamic monitoring of the state of transport infrastructure. The results could be implemented in promising intelligent control systems in the field of modeling human conscious behavior when sorting data required for the perception of environmental features

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Ontology-Oriented Multy-Agent System for Decentralized Control of UAV's Group

GLADUN,

KHALA

2024

Kibern. vyčisl. teh.

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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 further 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 MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, 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. This is 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 were 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 were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was 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 were 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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Intellectual Control, Localization and Mapping in Geographic Information Systems Based on Analysis of Visual Data

Cited by 3 publications

References 5 publications

Experience in Development and Implementation of Intelligent Systems for Control of Dynamic Objects

Experience in Development and Implementation of Intelligent Systems for Control of Dynamic Objects

Devising an image processing method for transport infrastructure monitoring systems

Ontology-Oriented Multy-Agent System for Decentralized Control of UAV's Group

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