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MULTI-AGENT INTELLIGENT SYSTEM FOR CONTROL OF PARKING SPACES IN CITY INFRASTRUCTURE
I. А. Pshenokova, К.C. Bzhikhatlov, М.А. Kanokova2025-04-27Abstract ▼With the growing number of cars and limited space, many cities are realizing the importance of implementing
intelligent parking systems to improve urban mobility and convenience for drivers. The level of
implementation of intelligent parking based on various technological solutions is growing, but to achieve
maximum efficiency, it is necessary to continue to develop technologies, integrate them with other systems
and take into account the needs of users. The purpose of the study is to develop a multi-agent intelligent
system for monitoring and managing parking space reservations in the city parking network. The architecture
of a multi-agent intelligent parking management system has been developed, which provides automatic
access control to parking spaces taking into account the wishes of parking lot owners, driver orders, the traffic
situation in the city and safety requirements. The main element of the developed system is parking, which
is represented by a set of parking spaces equipped with automated parking space management systems (parking
attendants), a communication system and data collection tools (surveillance camera and weather stations).
Parking spaces and parking attendants are managed by an intelligent control system based on multiagent
neurocognitive architectures. A prototype of a hardware and software complex of a multi-agent intelligent
parking space management system has been developed in the form of a client-server architecture.
The server is responsible for collecting, processing, storing data and managing automated parking attendants.
Two types of clients are connected to the server - a mobile application of the administrator and the
driver. The administrator has the ability to manage parking (set fixed prices or use server recommendations,
book parking spaces for employees) and view statistics (current load, parking statistics, data on accepted
payments, parking work forecast, recommendations). The driver has the ability to view the status of parking
in the area of interest (number of free spaces, waiting time for a free space, cost, recommendations for the
most convenient parking) and book a parking space with the ability to pay online -
HYBRID METHOD FOR SOLVING THE MULTI-AGENT TRAVELING SALESMAN PROBLEM
V.А. Kostyukov, F.А. Houssein2025-04-27Abstract ▼In this research work, the problem of task allocation in a multi-agent system is considered, where
each agent is a robot, and each task is represented by a position, which should be visited by one agent.
This problem is very similar to the multi-agent traveling salesman problem, which, unlike the famous traveling
salesman problem, involves several traveling salesmen who visit a given number of cities exactly
once and return to the starting position with minimal travel costs. Therefore, the multi-agent traveling
salesman problem is analyzed as a representative of the task allocation problem. The multi-traveling
salesman problem is important for the field of route optimization and task allocation between several
agents. It includes two different, but interrelated subproblems: distribute cities among agents and determine
the order in which each agent visits cities. In the literature, there are 3 concepts for solving this
problem with respect to solving its two constituent subproblems: the optimization concept, where both
subproblems are solved simultaneously; The Cluster-First, Route-Second concept is where the question of
which tasks to assign to which salesman is first decided, and then the question of the order in which each
salesman solves his tasks is decided; The Route-First, Cluster-Second concept is where the question of the
order in which tasks should be visited is first decided, and then this cycle is divided between agents without
changing the order of visits in order to answer the question of which tasks each agent takes on. This
paper proposes a hybrid approach to solving the multiple traveling salesman problem (mTSP), which
combines the ideas of two well-known concepts: "First clustering, then routing" and "First routing, then
clustering" in order to obtain their positive aspects and get rid of their weaknesses. To evaluate the effectiveness
of the developed method, a comparative study was conducted using the classical method for solving
the multi-traveling salesman problem. The results were evaluated based on three key criteria: the
computational time to obtain a solution to the multi-travelling salesman problem, the total length of the
routes travelled by the salesmen, and the maximum route length among them. The analysis of the experimental
data showed that when using the proposed method, the maximum path length among the routes
travelled by the agents (load imbalance) is reduced by an average of 26%. -
THEORETICAL FOUNDATIONS OF CREATING SELF-ORGANIZING DISPATCHERS OF DISTRIBUTED SYSTEMS BASED ON A MULTI-AGENT SOCIO-INSPIRATIONAL APPROACH
A.I. Kalyaev2021-11-14Abstract ▼This article describes new principles of organization, methods and algorithms for the functioning
of the Distributed System (DS) dispatcher, which allow allocating and reallocating resources
with dynamically changing parameters between incoming tasks in order to minimize their
execution time. The main problem that does not allow today to effectively estimate the execution
time of tasks in a heterogeneous DS directly follows from the distribution of the system: each of its
elements has partial independence and may differ significantly from others, moreover, in the process
of operation, its capabilities may change, and all this is essential. affects the efficiency of
distribution of tasks between DS nodes and the time it takes to complete tasks. The article proposes
a new approach to organizing a DS dispatcher, based on the application of the theory of multiagent
systems and socio-inspirational (based on accepted in human society) methods: DS users
place their tasks on special nodes – bulletin boards, a proactive software agent is placed on each
DS node, which implements constant monitoring of the parameters of your site and search on message
boards suitable for solving problems. At the same time, the agents participating in the solution
of the common task form communities in which they plan the process of solving the task and
the distribution of parts of the tasks to minimize the delay time for their solution. As a criterion for
the effectiveness of the DS, it was decided to take the value of the average delay in the execution of
functional tasks relative to the required points in time, respectively, the agents distribute tasks in
such a way as to minimize the value of the specified criterion. This article includes an introduction,
a formal statement of the task of scheduling DS resources, a review of existing approaches to
organizing a DS dispatcher, a description of the proposed multi-agent solution to the task of
scheduling DS resources using a socio-inspirational approach, an algorithm for the operation of a
distributed system and its elements, a description of the application of a socio-inspirational approach
in relation to the task scheduling process. and conclusion. The main advantages of the
proposed approach include: the ability to use reliable and up-to-date information about the specialization
and current performance of resources in dispatching; high fault tolerance due to the
absence of DS elements, failure of which leads to a complete loss of DS performance; the possibility
of flexible scaling of the DS (increasing the number of resources), achieved by decentralizing
the dispatching process. -
MULTI-AGENT ALGORITHM FOR AUTOMATIC DETECTION AND TRACKING OF NON-DETERMINISTIC OBJECTS
V.A. Tupikov, V.A. Pavlova, V.A. Bondarenko, A.I. Lizin, D.K. Eltsova, M.V. Sozinova2020-07-10Abstract ▼In order to develop a robust algorithm for the automatic detection and tracking of non-deterministic objects for embedded computing systems, in this work, a study and analysis in the field of state-of-the-art general-purpose automatic tracking algorithms is performed. The most successful of those algorithms suitable for long-term stable automatic tracking of objects (without a priori knowledge of the type of object being tracked) have already gone beyond solving exclu-sively tracking problems, and include a synergistic combination of several heterogeneous tracking algorithms, as well as at least one automatic detection and / or classification algorithm. Thus, the authors of the article conclude that the most stable modern automatic tracking algorithms are a multi-agent system that makes a decision about the current position, size and other parameters of the tracked object image based on intelligent voting of system’s submodules that independently monitor the object and form its model. Individual models of each of the submodules are updated based on the results of a collective decision. The authors of the study identified the most effective of the applied basic algorithms suitable for use in embedded computing systems of robotic systems, and developed a new multi-agent algorithm for the automatic detection and tracking of non-deterministic objects. The presented multi-agent algorithm includes a submodule for extracting and matching key points in images, a clustering and filtering submodule for key points using the DBSCAN algorithm, a tracking submodule based on the optical flow calculation algorithm, and a key point classification submodule. A semi-natural testing of the developed algorithm was carried out and its effectiveness in solving tasks not only of automatic tracking of objects, but also in tasks of automatic objects detection using several reference images were evaluated. In conclusion, the authors present steps for further improving the accuracy and performance of the developed algo-rithm for its forthcoming implementation for on-board computing systems of aerial vehicles.
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DEVELOPMENT OF A METHOD FOR SOLVING THE PROBLEM OF TASK ALLOCATION IN A MULTI-AGENT SYSTEM
V.А. Kostyukov , F.А. Houssein144-1552025-10-01Abstract ▼This paper considers the problem of task distribution within a multi-agent system, where each agent is an autonomous robot, and each task corresponds to a point in a two-dimensional environment that one of the agents must visit. This problem is essentially similar to a multi-agent version of the classical traveling salesman problem, where several agents are involved instead of one participant. Each of them must go through a unique route covering a certain set of points. In this regard, a study of the multi-agent traveling salesman problem is conducted as one of the formats for setting the problem of distributing goals among agents. This problem is of great importance in the field of routing and optimal task distribution. Its solution includes two closely related subproblems: determining the set of points assigned to each agent and constructing the optimal route for visiting them. There are three main approaches to solving this problem in the scientific literature: Optimization approach, where both subproblems are solved jointly; Cluster-First, Route-Second model, where tasks are first distributed among agents, and then routes are built;
The Route-First, Cluster-Second model assumes initial optimization of the route for all points with its subsequent division between agents without changing the order of visits. In this paper, a hybrid method is proposed that combines elements of the Cluster-First, Route-Second and Route-First, Cluster-Second approaches. The goal is to combine the strengths of both concepts and minimize their drawbacks. To test the effectiveness of the developed method, a comparative study was conducted. The evaluation was carried out according to three main metrics: the time spent on constructing a solution, the total length of all routes, and the maximum route length among all agents. The experimental results showed that the use of the proposed method allows for a reduction in the maximum route length (thereby reducing the load imbalance between agents) by an average of 26%. -
NATURAL LANGUAGE CONTROL OF CONSTRUCTION ROBOTIC SYSTEMS
D.G. Makoeva , I. R. Tlupov , А. О. Shogenov83-932025-11-10Abstract ▼The study aims to investigate the potential of natural language control systems for construction robots. It is the lack of reliable natural language processing systems that serves as a limiting factor that prevents intelligent robotics from fully realizing its potential. The work provides an overview of modern robotic construction systems that are used to facilitate and improve construction and engineering processes and tasks. What unites all these systems is the lack of natural language control. In this paper, we present principles, algorithms, and methods that allow an intelligent agent to penetrate the essence of the context of a situation unfolding in the field of construction and engineering tasks. The approach is based on a multi-agent neurocognitive architecture, which serves as a kind of tool for modeling the process of automatic interpretation of phrases taken from a limited subset of natural language. In order for an intelligent agent to correctly interpret an incoming message, it must accurately determine the conditions, actions, properties, and relationships that take place in the "intelligent agent - environment" system. Only then does the agent gain the ability to interpret the context of the current dialogue and generate statements necessary for designing cooperative behavior aimed at jointly overcoming technical obstacles. One of the most common problems requiring a solution in the rapidly developing field of robotics is the development of a dialogue control system capable of coordinating joint human-machine behavior and interpreting goals and mission conditions set out in natural language. A control system based on natural language is an integral part of an intelligent system, the foundation of which is a self-organizing multi-agent neurocognitive architecture. Its main goal is to establish seamless communication between human-machine teams so that they can jointly set, describe and successfully complete complex construction tasks. The fundamental element of the approach is multi-agency, which allows the robot's decision-making system to be flexible, adaptive and continuously expand the range of its knowledge, generating questions necessary for further work.
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MULTI-AGENT ARCHITECTURE OF AN ENVIRONMENT REPRESENTATION SYSTEM FOR AN AUTONOMOUS AGRICULTURAL ROBOT
К.C. Bzhikhatlov , I. А. Pshenokova2026-04-29Abstract ▼The relevance of this research stems from the need to create effective control systems for autonomous robots capable of operating in uncertain and dynamically changing environments. An environment representation system must address the challenges of localization, mapping, object detection and classification, dynamic prediction, and semantic interpretation. For an autonomous robot to navigate and perform goal-directed actions in its environment, it must understand its environment—without this, it will be unable to effectively plan movements, avoid obstacles, and reach destinations. Existing environment representation methods have limitations when adapting to unfamiliar, unstructured environments. The aim of this study is to develop the concept and architecture of an environment representation system based on a multi-agent neurocognitive architecture for controlling autonomous robots within a heterogeneous human-machine team. The scientific novelty of this study lies in the development of a multi-agent neurocognitive architecture for representing the state of the environment. The proposed approach enables the creation of flexible world models capable of self-organization and scalability with increasing knowledge. The research methodology is based on the use of multi-agent technologies and neurocognitive models. A multi-agent neurocognitive architecture has been developed, including mechanisms for collecting data from sensors, representing objects and subjects in the environment, forming a mechanism for constructing cause-and-effect relationships, and sharing knowledge between members of a heterogeneous human-machine team. The developed architecture enables the scalability of decision-making systems and facilitates knowledge sharing between team members. A promising direction for further research is improving the system's safety mechanisms. The results of this study can be used in the development of next-generation autonomous robotic systems.
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