No. 1 (2024)

The problem of safe movement of an autonomous underwater vehicle (AUV) in the presence of
stationary obstacles is considered. Traditionally, information about an obstacle is generated as the
AUV approaches the obstacle, and using it, the AUV control system makes a decision on the parameters
of the AUV’s further movement (course, speed, depth). The goal of the work was to determine the
spatial path to bypass the obstacle based on determining the geometric shape and size of the obstacle
according to digital maps. The paper proposes a method for determining a spatial 3D path to bypass
an obstacle, using complete information about the geometric shape and size of the obstacle, obtained
by supplementing the data from the means of illuminating the situation with data from digital bathymetric
maps of the areas through which the AUV route runs, as well as digital physical maps of the
areas of the earth. surfaces indicating small islands protruding onto the sea surface. The bathymetric
map isobaths are constructed from measurements at grid nodes covering the area under consideration;
the grid spacing exceeds hundreds of meters. To assess the probability of occurrence of bottom
topography anomalies between grid nodes that pose a danger to the movement of AUVs, it is proposed
to use the method of fuzzy probabilistic analysis. Based on the nodal points covering the obstacle,
a two-dimensional autocorrelation function is calculated, and the values of linguistic variables
are formed. Based on these variables, production rules were formed and, using them, the probability
of occurrence of relief anomalies was determined. To determine the shortest distance, the existing
depth grid at the node points of the obstacle is presented in the form of an oriented weighted graph:
the graph nodes are grid nodes with known depths, the edges are assigned weights equal to the spatial
distances between the three-dimensional grid nodes (latitude, longitude, depth). The developed
algorithm for determining the path to bypass an obstacle consists in determining the end point of the
bypass on the route trajectory behind the obstacle and finding the shortest path to bypass the obstacle
by comparing the current path under consideration with those obtained previously. If the length of
the path under consideration exceeds the length of the intermediate node of the previously formed
path, the process of reviewing the current path stops, and the transition to the consideration of the
next path is carried out. The results of the numerical experiments showed that the reduction in the
path around the obstacle compared to the traditional approach in the considered example was 17%.

The purpose of the study is to develop a method of interaction of a vessel with underwater
cargo for both loading and transporting. The article presents the findings of the research on reacting
a unit for lifting underwater cargo onto a vessel. The image of the unit was formed on the basis
of an assessment of possible technical solutions, theoretical findings and modelling. The analysis
of the previous research on creating alternatives is being carried out in the given research. To
dock with underwater cargo from the vessel, a special receiving module is lowered at four suspension
supports, a cable suspension scheme is of great preference. Four mechanisms have been developed
to form the basis of the unit such as the lifting mechanism, the compensation mechanism,
the damping mechanism and the locking mechanism. The lifting mechanism is based on electric
winches on electric asynchronous motors worked on vector control. The rope of the lifting mechanism
is bound through the pulley to the load. To compensate for the disturbances caused by the
rolling of the carrier vessel, a hydropneumatic system is included in the rupture of the cable line,
which fends off the emerging dynamic loads by moving the rods of the hydraulic cylinders. The
damping mechanism absorbs the energy of the impact of the platform of the descent module with
the hull of the vessel in the mooring mode. The locking mechanism ensures reliable fastening of
the descent module with or without underwater cargo in a stowed position with the hull of the
carrier vessel. The model of an asynchronous motor with a short-circuited rotor is obtained from a
generalized circuit by shorting the rotor windings. A frequency control method is provided, the
rotor flow coupling vector is taken as the base vector. The cable suspension model takes into account
its deformation during movement during operation. The compensation mechanism model is
based on an adiabatic process in a macroscopic system, in which the system does not release any
heat to room. While calculating and modeling, the parameters of the nodes and mechanisms are
selected in such a way that technically feasible conditions for the operation of the unit are provided.
The loads on the cable system are limited and its sagging is excluded, the stroke of the compensator
carriage is minimized. As a result, a quasi-uniform lifting of the underwater cargo was
obtained with minor speed fluctuations during the rolling of the carrier vessel.

The experience of modern combat operations has initiated the high relevance of the tasks of airdelivered
assessing the dynamics of changes in time characteristics of groups (clusters) of objects of
interest on the ground. The active development of unmanned aviation, including within groups, provides
new opportunities for periodic monitoring of the area with the solution of problems of detection and
recognition of clusters of objects of interest in dynamics. The article analyses the possibility of using the
theory of resemblance to solve the problems of assessing the similarity of types of weapons, military and
special equipment by the nature of distribution in various clusters, including in various geographical
conditions. It is shown that the dynamics of objects can be established by regular monitoring of the terrain
with the estimation of various measures of similarity and difference for clusters. At the same time,
the applicability of well-established statistical methods of biodiversity research developed in biology to
assess the diversity of population, their complexity, similarity, relationships, etc. is proved. The characteristics
of the species diversity of the most important deterministic clusters of troops and equipment of
NATO countries are given. The efficiency of the proposed approach demonstrated by the example of
aerial reconnaissance of a conditional area with recognition of the dynamics of five types of clusters,
including various types of military personnel, personnel and engineering equipment. General recommendations
for conducting appropriate assessments and decision-making are given.The following basic similarity measurements are recommended for use: Jacquard similarity coefficients to determine the
similarity level of clusters by their constituent types of VVST samples (cars, tanks, guns, armored vehicles,
etc.), the Margalef index to determine the number of types of VVST in the total number of VVST
units in the cluster, the generalized Shannon diversity mass to assess the diversity of species in a cluster,
the Sorensen-Chekanovsky coefficient is used to determine the degree of occurrence of the selected type
of samples in the cluster. It is advisable to use the obtained results in multi-criteria tasks of preflight and
operational planning of group operations of unmanned aerial vehicles in the interests of monitoring the
controlled territory, taking into account the required schedule for obtaining reliable information.

In the modern system of armed conflict, where hybrid and informational methods of solving
combat tasks are relevant, it becomes increasingly important to develop effective methods for assessing
the combat capabilities of reconnaissance-strike robotic systems. This research is aimed at
creating a universal method for evaluating such systems in real combat conditions, ensuring a comprehensive
approach to measuring their effectiveness. The authors emphasize the integration of algorithmic
solutions designed to analyze the effectiveness of modern weapons and military equipment,
allowing for a wide range of variables and tactical-technical characteristics typical of the current
combat situation. The work pays special attention to identifying key characteristics of ground combat
robotic complexes and investigating their use in groups. This opens paths for increasing combat
effectiveness, reducing risks for personnel, and improving decision-making processes. Considering
the implementation of autonomous technologies, the study highlights the significance of robotization
in the context of military actions, focusing on the necessity of using machines in high-risk areas for
humans. By analyzing existing methodologies for assessing the combat effectiveness of reconnaissance-
strike samples of weapons and military equipment, the authors proposed an algorithm that
takes into account the unique requirements and characteristics of robotic systems, including their
firepower, mobility, and survivability. This algorithm can become the basis for the development of
control systems for the next generation of robotic complexes, ensuring their increased combat effectiveness
and ability to work effectively as part of groups in military operations. Thus, the results of
this research represent a significant contribution to the field of military robotics, offering approaches
that will help in the development and optimization of robotic combat systems. These developments
can serve as a basis for improving strategies for the use of such systems on the battlefield.

The purpose of the study is to form a geometric model of the environment containing information
about the parameters of the underlying surface for use in a system for planning the movements
of a group of robots in formation at high speed. The article examines the problem of con structing a map of support surfaces. An analysis of existing research on the topic of determining
the characteristics of supporting surfaces by mobile robots is presented. A classification of methods
for assessing the characteristics of a supporting surface into remote and contact ones is given.
Based on an analysis of the advantages and disadvantages of known remote and contact methods,
the work proposes a combined approach that makes it possible to use the advantages of both
methods. The approach is based on remote division of space into clusters according to the external
parameters of the underlying surface with potentially identical internal properties, simultaneous
determination of the internal parameters of the underlying surface by the contact method and their
further combination. In this case, the surface parameters are constantly updated during movement.
The approach uses a limited list of standard on-board means of a mobile robot and does not require
large computational costs compared to machine learning methods. A description is given of
the remote determination of the external parameters of the underlying surface, which are based on
point cloud segmentation algorithms that do not require preliminary training. The arguments for
segmentation are: the coordinates of the cloud points, the color of each point, and the height difference
in the vicinity of each point. An algorithm for determining the internal characteristics of a
surface using the contact method is described. The friction coefficients between each wheel and
the current surface are considered as internal parameters. These coefficients make it possible to
determine the maximum accelerations for each robot in the group, which are necessary to implement
the motion planning system. The paper presents the results of experimental studies of remote
determination of the parameters of the underlying surface within the framework of the proposed
approach using data from the public KITTI dataset. The results of the study confirm the possibility
of forming a geometric model of the environment, segmented into areas with different characteristics
of the supporting surface without training using standard hardware capabilities of the robot

An approach is proposed to substantiate the parameters of a robotic complex designed for
emergency rescue and other urgent work in the aftermath of accidents at particularly dangerous
facilities (hereinafter – RTC). This approach is based on the formulation of an ideal model of a
robotic complex and its further transformation in order to ensure the possibility of creating, on the
basis of existing industrial technologies, a promising sample that surpasses existing robotic tools
in terms of performance. The chosen approach defines a number of provisions regarding the process
of substantiating the parameters of technology and the creation of its promising samples and
technologies, including theoretical provisions that should be the basis for its consideration; methods
of action in its organization; the algorithm of its implementation. In the course of achieving
this goal – substantiating the parameters of the RTC under consideration, the following scientific
tasks were solved: the problems of creating a model for optimizing parameters were analyzed, an
"ideal model" for optimizing parameters was developed, on the basis of which a rational model for
optimizing RTC parameters was prepared. When solving these tasks, the main features of ideal
RTCs were identified: autonomy, flexibility, intelligence, maneuverability, programmability, possession
of sensory feedback. In addition, ideal RTCs should ensure: the exclusion of deaths and
injuries of personnel during emergency situations (hereinafter referred to as emergencies); improving
the effectiveness of emergency rescue and other urgent work (hereinafter referred to as
EROUW); flexibility and adaptability during EROUW. The rationalization of an ideal model in
this article is proposed to be understood as its transformation by reducing the requirements for the
corresponding parameters. At the same time, two types of rationalization of RTC parameters are
considered: – according to tactical indicators, it is a model that is advisable to implement if a
stochastic mathematical model of RTC actions can be developed; – according to technical indicators,
the model used if the mentioned RTC action model is not created. This approach makes it
possible to assess the degree of deviation of the selected RTC parameters and the methods used to
justify them from the best options, as well as to outline ways to improve them.

Organizing continuous monitoring of large spaces with dynamically changing conditions and
conditions is one of the key tasks in various areas of human life. This task is especially acute in Russia,
taking into account its territories (lands) intended for agricultural activities. The particular importance
of organizing continuous monitoring is also emphasized by the development of the concept and technology
of precision farming. As a means to solve this system problem, various robotic and unmanned systems
can be used, equipped with the necessary equipment in accordance with the local tasks of continuous
monitoring. Continuous monitoring can only be ensured by the use of effective algorithms for constructing
the movement trajectory of the mobile robotic and unmanned (primarily aviation) systems
used. Increasing the efficiency of such algorithms from a mathematical point of view is always complicated
by the cyclical nature of motion trajectories, i.e. construction of a Hamiltonian cycle. This work
proposes a method for constructing an optimal trajectory for continuous cyclic monitoring tasks of agricultural
fields. The method is based on finding a Hamiltonian cycle on the graph of the terrain map and
allows for the automatic construction of an optimal closed path for any terrain map. A distinctive feature
of the method is the use of a modified algorithm for finding Hamiltonian cycles. The algorithm can
be scaled for maps corresponding to graphs with a large (more than 100) number of vertices, for which
the standard brute-force algorithm for finding Hamiltonian cycles requires significantly more execution
time than the proposed algorithm. It is shown that the algorithm used has a 17 times smaller growth
constant in time complexity compared to the standard algorithm for finding Hamiltonian cycles. This
allows for an increase in the number of vertices in the graph used for finding Hamiltonian cycles in
real-time mode (0.1-100 seconds) by an order of magnitude (from 30 to 500). The developed algorithm
can be implemented in modern unmanned monitoring systems for optimizing the trajectory of agricultural
fields monitoring by unmanned vehicles in real-time mode, thus contributing to the dynamically
evolving field of precision agriculture

An analysis of the state of research has shown that currently restorative mechanotherapy is
widely used in the rehabilitation of patients with functional disorders of the musculoskeletal system
caused by the consequences of vascular diseases, disorders of neuroregulation of motor activity,
injuries and pathology of the musculoskeletal system. In restorative mechanotherapy, I most
often use robots of a sequential structure that have the necessary working area, but at the same
time have a low load capacity, as a result of which the system has to be scaled. Parallel robots are
an excellent solution for the implementation of mechanotherapy based on robotic tools. The article
presents the structure and model in two versions: a single-module robotic complex (RTC) for the
rehabilitation of one limb and a two-module robotic complex for the rehabilitation of both limbs.
Each module includes an active 3 - PRRR manipulator to move the patient's foot and a passive
orthosis based on an RRR mechanism to support the lower limb. Based on the clinical aspects in
the field of rehabilitation, the requirements for the developed RTC for the rehabilitation of the
lower limbs are formulated, taking into account the anthropometric data of patients. A mathematical
model has been developed describing the dependence of the positions of the links of the active
and passive mechanisms of the two modules on the angles in the joints of the passive orthosis,
taking into account the options for attaching kinematic chains of active manipulators to mobile
platforms and their configurations. A method of parametric synthesis of a hybrid robotic system of
modular structure has been developed, taking into account the formed levels of parametric constraints
depending on the ergonomics and manufacturability of the design based on a criterion in
the form of a convolution comprising two components, one of which is based on minimizing unattainable
trajectory points taking into account the features of anthropometric data, and the other on
the compactness of the design. A digital RTC twin and an outboard safety mechanism as part of
the RTC have been developed using CAD/CAE tools of the NX system. The design of the passive
RRR mechanism was carried out by reverse engineering using 3D scanning. The results of mathematical
modeling, as well as the results of analysis, are presented

Operating organizations and potential consumers of aviation works and services may not
fully benefit from the introduction of non-emergency or highly automated aircrafts, introducing
them only locally, within the framework of traditional business models of aviation application,
often even in fixed volumes characteristic of aircraft of previous generations, with a higher cost of
operation. The aim of the work is to define the boundaries of the field of formation of integrated
aviation systems (IAS) as an effective mechanism for the introduction of aircrafts control automation
technologies, when the introduction of unmanned or highly automated aircrafts will be effective
in the formation of IAS. Based on the optimal patrol model, the numbers of aircraft fleet without
the formation of the IAS and with the formation of the IAS are calculated. The values of operating
costs and the values of fines and losses from emergencies with the formation of IAS with
unmanned and (or) highly automated aircrafts and without the formation of IAS with unmanned and (or) highly automated aircrafts and with manned aircrafts are determined. The dependence of
the IAS formation efficiency area on the studied parameters is determined. Parametric calculations
have been performed for the characteristic values of the parameters. It is shown that the
maximum economic effect can be achieved by the formation of optimal IAS with unmanned or
highly automated aircraft, in which, according to the global economic criterion, both the aircraft
fleet and its application strategy and the "response" business processes of the consumer of aviation
works and services are optimized. The boundaries of the cost values for the introduction of
automation technologies are determined depending on the length of the patrolled highway, when
the introduction of unmanned or highly automated aircraft will be effective only when forming an
IAS, and without the formation of an IAS, and when the introduction of unmanned or highly automated
aircraft will be ineffective, manned aircraft will be effective. Thus, an effective mechanism
for the dissemination of control automation technologies and improving the efficiency of business
processes in various industries is the formation of IAS by aviation equipment development organizations
and manufacturers, in the interests of potential consumers of aviation works and services.

Unlike existing methods of teaching artificial intelligence systems, approaches based on federated
learning will not require a long and expensive procedure for preparing a training sample when
creating and mass practical application of "smart" agricultural systems, autonomous unmanned
agricultural machines and robots, and the knowledge obtained by the decision-making system will be
updated on an ongoing basis. The aim of the research is to develop and implement end-to-end artificial
intelligence technology, the lack of which today prevents the creation of integrated information
management systems for crop and livestock production ("smart" agricultural systems) based on the
group application of unmanned ground and aerial agricultural machines and robots. The introduction
of such intelligent systems is necessary to preserve and improve the products produced and ensure
the sustainable development of agriculture. The article describes neurocognitive methods and
algorithms of federated learning of intelligent agricultural process management systems in a real environment. The structure of data and knowledge exchange in the smart field system based on a
distributed network of intelligent agents managing smart field systems on various agricultural lands,
based on federated learning, is also proposed. Each intelligent agent is a software model of the neurocognitive
processes of reasoning and decision-making within the framework of solving a specific
task. The proposed structure will facilitate the joint accumulation of a knowledge base in the field of
agriculture and will be able to become the basis for many different intelligent agents that effectively
perform specific tasks within a distributed network of smart field management systems. There is also
a description of intelligent agents performing various tasks in a real environment. Examples of autonomous
robotic and software complexes being developed are given, on the basis of which it is
planned to test the proposed concept of federated training of "smart" field systems. At the same time,
the article describes the expected effects of the introduction of technologies based on the developed
methods and algorithms of federated training of intelligent agents controlling smart field systems.

The paper proposes a method for constructing continuous control of non-affine control objects
with differentiable nonlinearities and a measurable state vector. The method is based on the use of
quasilinear models of nonlinear objects, which are created on the basis of their equations in Cauchy form while maintaining the accuracy of the description. It is shown that control by state and influences
exists if the nonlinear object is completely controllable by state and satisfies the criterion of
output controllability. To determine the control, it is necessary to find a number of polynomials using
the object model and solve polynomial and nonlinear algebraic equations. The method is analytical
and allows us to provide some primary quality indicators. The region of attraction of the equilibrium
position of a closed system is determined by the region of state space in which the controllability
condition of the quasi-linear model of the object is satisfied. Depending on the nonlinearity properties
of the object, control is defined either as a function of state and deviation variables, or is a numerical
solution obtained by an iterative method. The required control is oriented towards implementation
by a computing device. The article provides the formulation of the problem, the conditions for
its solvability, as well as analytical expressions for finding the control action. A numerical example is
given with the results of synthesis and modeling, which allows us to conclude that the above relations
lead to finding continuous control of a non-affine object with differentiable nonlinearities and a
measurable state vector, which ensures the required properties of a closed-loop control system

The paper considers a problem of area coverage by unmanned aerial vehicles (UAV) using
mobile charging stations. Practical area coverage tasks require a simultaneous use of several
UAVs in order to optimize time consumption during a mission. Another limiting factor in UAVbased
coverage is a duration of a UAVs’ single-battery autonomous operation. To complete a
large territory coverage mission static or mobile charging stations could be employed in order to
recharge or replace an onboard battery. Static charging stations cause a mission interruption and
increase time required to complete the coverage mission. It is also important to choose proper
locations in the case of static charging stations. However, a process of installing the charging
stations is time-consuming, which makes them impractical for missions where coverage must be achieved within a short period of time, e.g., such as rescue or emergency search operations. Mobile
charging stations can move around the area to optimize a UAV battery recharging or replacing
process. A challenge of the latter case is to plan motion trajectories not only for UAVs but also
for a mobile charging station. Joint motion planning improves coverage efficiency but increases
computational complexity of a trajectory planning. This paper considers a problem of efficient
area coverage with multiple UAVs and a mobile charging station using a genetic algorithm.
To adapt a genetic algorithm for the coverage problem, a chromosome formation method is proposed
in the paper. The method allows encoding trajectories of the UAVs and the mobile charging
station, and takes into account time and a place of charging (replacing) the UAV batteries. To
evaluate the proposed approach, a software was developed in Python programming language. The
obtained results of the simulation demonstrated feasibility of the proposed approach

Nowadays, the navigation systems of robot-technical complexes (RTC) use heterogeneous sensors
of primary information, which can provide redundancy of navigation data. This allows to increase
the accuracy of calculation of motion parameters, as well as allows to determine them with greater
reliability in case of failure of one or more sensors. The paper gives a review and classification of lowlevel
mathematical methods of processing overridden state parameters of RTC navigation systems. It is
noted that the problem of combining is a subfield of the problem of system identification and therefore
has common approaches to the construction of the solution. In the vast majority of methods based on the
optimization approach, the quadratic error function is used as the optimality criterion. All mathematical
methods of combining (complex processing or fusion) any data are divided into low-, medium- and
high-level methods. In navigation systems, low-level methods such as recursive, nonrecursive, and covariance-
based methods are the most used. Non-recursive methods are rarely used directly. Recursive
ones are usually constructed using a Kalman filter scheme. Recursive ones, as a rule, are constructed
according to the Kalman filter scheme. Not all methods are robust to non-Gaussianity and correlation
dependence of the original data, which is often encountered in navigation systems with overdetermined
data. In addition, not all methods can be used to address the relevance of data from navigation instruments.
It is noted that the key for combining methods is the approach of fusion data in an information
space, understood as the inverse of covariance, since the vast majority of methods, including Bayesian
methods, are reducible to it. In this regard, covariance-based methods are of most interest. However, for solving the problem of data relevance in navigation, the existing methods are poorly suited to the problem
of data relevance because they require computationally intensive optimization problem solving at
each step, and navigation systems are real-time systems. Thus, there is a problem of developing new
approaches to solve this problem

The main way to solve problems of planning and traffic control is the use of intelligent technologies.
At the same time, intelligent technologies are used to solve the problems of setting and adjusting
control goals and action programs to implement these goals, as well as to form a control
algorithm under conditions of uncertainty caused by various factors in actuators, the motion control
subsystem, and the planning and behavior subsystem. This work is devoted to the actual problem of
mathematical modeling and control theory: the problem of decentralized control of a multi-agent
system consisting of agents modeling the behavior of autonomous robots in order to ensure the movement of a group of robots deployed in a line and in a «convoy» type formation. The paper examines
the results of research in the field of controlling a group of unmanned aerial vehicles, identifies
the types of tasks that can be performed by a group of aerial robots, and highlights the main control
strategies and their features. The general positions necessary for the development of a detailed group
control algorithm have been formed. Each robot must navigate in space autonomously without GPS
using signals from its own camera or lidar (active rangefinder), identify obstacles, build optimal
paths of movement and make decisions aimed at achieving the goal and completing the task. Management
is carried out using an alternative collective adaptation algorithm, based on the ideas of
collective behavior of adaptation objects. To implement the adaptation mechanism, the vector parameters
are matched with adaptation automata that model the behavior of adaptation objects in the
environment. A structure for the process of alternative collective adaptation has been developed,
under the control of which a group of robots moves in formation

Information about fire-hazardous situations circulating in the circuits of the control
and control systems of the Navy ship and the level of artificial intelligence technologies is quite
enough to develop a scientific and methodological apparatus for detecting fire-hazardous situations
in ship premises, determining the location of their occurrence and fire factors, predicting the development
of a fire-hazardous situation and developing a set of technological solutions using artificial
intelligence to obtain sound recommendations on localization and extinguishing fires on Navy ships.
This will significantly reduce the time for detecting sources of ignition, provide reliable information
about the fire-hazardous situation, predict the development of a fire in the ship's premises and
promptly organize the fight against a ship's fire before the occurrence of critical fire-hazardous factors
and damage to the ship, the health and life of personnel. Artificial intelligence technologies are
an effective means of solving complex poorly formalized tasks. This class traditionally includes the
tasks of classification, clustering, approximation of multidimensional maps, time series forecasting,
nonlinear filtering, and management of complex technological objects. The analysis of the fire hazard
of technological processes, the operation of ship systems and technical means has shown that one of
the most promising ways to resolve the systemic contradiction in ensuring fire safety is the use of
artificial intelligence technologies. The need to develop intelligent survivability systems on Navy
ships is due to the need to improve the effectiveness of leadership in the fight for survivability in a
number of accidents and catastrophes. Examples of the influence of various factors on the conduct of
the struggle for survivability in the event of accidents are described. The role of intelligent survivability
systems in the systems of ships and vessels is determined. The necessity of implementing such systems
is justified. The intelligent survivability systems currently being developed on Navy ships are
designed to assist the command staff of ships and vessels in the timeliness and validity of decisionmaking,
which will increase the effectiveness of the fight for survivability

This research paper analyzes the multi 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 their original position with minimal travel costs. The multi-traveling salesman
problem is an important problem in the field of route optimization and task distribution among multiple
agents. The main goal of the study is to develop an effective method for solving this problem, which will
reduce task completion time and optimize the use of resources. During the study, an innovative method
was created based on reducing the dimension of the solution space. This method allows you to more
efficiently manage workload and resources, which in turn helps to minimize the overall execution time of
tasks. A special feature of the method is its versatility and applicability in various scenarios, including
situations with different numbers of tasks and traveling salespeople. This approach provides broader
coverage and allows the applicability of the method to be assessed in different contexts, which is an
important strength of this study. 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 calculation time for solving the multi-traveling
salesman problem, the total length of the routes traveled by the traveling salesmen, and the maximum
route length among them. Analysis of experimental data showed that the developed method significantly
exceeds the classical approach in all considered criteria in most experiments, since when using the
proposed method, the average time for calculating a solution to the multi-traveling salesman problem is
reduced by 56%, while the average sum of the lengths of routes traveled by traveling salesmen is reduced
by 12%. In addition, the maximum path length among the routes traveled by agents (load imbalance)
is reduced by 8%, which confirms the high efficiency of the proposed method and its promise for
practical application in various fields where optimization of routes and distribution of tasks among
several executors is required

Continuous improvement of the technology for creating unmanned aerial platforms leads to
an increase in their quantitative composition and the tasks they solve. Installing jamming elements
as a payload on unmanned aerial vehicles (UAVs) makes it possible to study the electronic suppression
of multifunctional radar devices (MRLS) by air defense systems due to the excess of the
jamming numbers over the number of oscillations of the jammer compensator. Since modern caliber and millimeter-wave wavy radars are connected to phased array antennas (PAA), most often
of the pass-through type, the task of increasing the anti-interference channel resource in such
conditions is most relevant. One solution to this problem is the constructive unification (integration)
of the main and compensation subarrays in a common phased array array. This solution
requires a small hardware and software modification, which consists in using a system of additional
receivers with a digital output operating on the main FAR network, which is more economical
when using auxiliary small-sized compensation FARs. The article presents the comparative
effectiveness of the subsystem when exposed to interference with subarrays of compensation elements
integrated mainly by phased arrays and an automatic interference compensator with a large
number of small-sized compensation arrays. The study was carried out by modeling using a simulation
software computer model of the electrical subsystem of periodic periodic interference of a
radar with phased array under the influence of a group of UAVs - carriers of the operating parameters
of the interference. The results of demonstrating the possibility of increasing the antiinterference
channel resource when implementing the proposals of the radar, as well as an increase
in the noise immunity indicator by 1.02...1.23 times compared to the radar, equipped with
small-sized phased array compensation channels

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions
of data packets received from several sources may occur, which leads to the loss of some or all
information. With the increase in the number of simultaneously operating hydroacoustic modems, physical
layer algorithms do not provide stable data transmission and the likelihood of collisions increases,
which makes the operation of modems ineffective or even impossible. To ensure effective operation in a
hydroacoustic signal propagation environment and to reduce or eliminate collisions during the exchange
and delivery of data between two modems that do not have the ability to operate synchronously,
as well as to reduce the access time to the signal propagation environment, methods of the medium access
control layer are required using link layer protocols. Typically, this problem is solved using code
separation of hydroacoustic channels. Modems communicate as if at different frequencies, which does
not create collisions, this allows subscribers of the underwater network to communicate in a point-topoint
format, or in multicast mode, that is, everyone separately, however, in case it is necessary to make
a transmission over the network , this option is no longer suitable, since network transmission involves
working on the basis of “broadcast” messages. In practical use, it is convenient to place these protocols
into a software development environment (framework) for specific user applications for solving network
communication problems. Such a framework is usually called a software framework; it allows for user
modification of the network algorithms available in the framework, as well as the inclusion of new network
hydroacoustic communication algorithms by the user. To build a predictive model, the DACAP, TLohi,
Flooding and ICRP protocols were used in the work. The algorithms were implemented in Erlang.
The paper presents algorithms for implementing these protocols. A comparative analysis of network
operation with and without protocols is provided. Efficiency and speed of work were assessed. Recommendations
for further development of the software framework are given

The recent increasing demand of heterogeneous groups of robots (UAV and UGV) with increased
autonomy when conducting special operations in industrial and urban environments is
substantiated. The urgent task of forming, based on data from UAVs on-board computer vision
systems, an information and navigation field that ensures autonomous targeted safe UAVs and
UGVs movement in shielded areas of an urban environment is formulated. The formation of a
generalized geometric model of the external environment can be achieved by specifying a set of
target positions in terms of the working area, which the UAV must visit in a given sequence and
return to the starting point. In the process of visiting achievable target points, a generalized geometric
model of the external environment is formed and the current coordinates of the UAV are
determined. Methods and algorithms for constructing various models of the external environment
and solving navigation tasks are described, which ensure planning and executing of targeted safe
movement trajectories in real time according to on-board data, which is the basis of autonomous
control, including the heterogeneous robots’ groups control. Autonomous control systems for the
movement of UAVs and UGVs are based on methods and algorithms for identifying semantic objects
(supporting surface planes and vertical walls), which abound in urban environments, and
extreme navigation using 3D images (point clouds), obtained from lidar or depth cameras.
The results of experiments on the information and navigation fields creation and solving navigation
tasks based on on-board computer vision data in a real industrial-urban environment are
presented, confirming the effectiveness and practical value of the proposed methods and algorithms.
The use of a single information and navigation field, on the one hand, significantly increases
the autonomy of a group of robots due to the ability to independently plan actions when
performing complex operations, and on the other hand, increases the situational awareness of
robot operators by providing information about the working space in a convenient form

The authors of the work carried out research in the field of technical vision systems, as well
as approaches to solving problems of detecting and tracking objects of interest without a priori
knowledge of their type, taking into account the target platform in the form of an embedded optoelectronic
system computer. Based on the data obtained, the sphere was analyzed and a new hybrid
maintenance algorithm for embedded systems was proposed. It is based on a combination of several
types of maintenance algorithms, with one of them as a priority, providing the main work, and
several auxiliary ones to stabilize and expand the functionality of the priority one. They are connected
by an external processing cycle, which, based on a consensus decision of internal algorithms,
independently decides on the position of the target object in the frame and stores auxiliary
information to ensure the correct operation of the entire algorithm, as well as responsible for making
a decision on the re-detection of the target. The authors propose two possible implementations
of this approach, used depending on the power of available computing resources. A variant of the
algorithm has been implemented for the available computing power, and its semi-natural tests
have been carried out based on real video sequences. They represent different backgrounds and
different structural objects of interest with different dynamics of change over time. The evaluation
of the results of the proposed algorithm in the tasks of detecting and tracking an object of interest
in real time on the presented videos using a software package for automating testing of detection
and tracking algorithms has been carried out. As a result, the algorithm showed high efficiency in
the tasks set, improving the accuracy of tracking, in comparison with internal algorithms that
worked separately, by adding rotary and scale invariances, and also significantly increased the
ability to re-detect an object after its loss. In conclusion, the authors present proposals for the
further development and implementation of optoelectronic systems into embedded computers

The present study introduces a method for algorithmic compensation of the displacement of
the centers of sensitive elements of accelerometers within a high-precision inertial navigation
system. Previous considerations omitted this compensation due to the potential for minimizing its
impact through structural features—specifically, the close proximity of accelerometers to each
other. With the upgrading of components in the inertial sensors, the influence of size-effect errors
could become significant compared to gyroscopes and accelerometers errors. This study aims to
analyze the impact of these errors on solving navigation tasks under the precision conditions of
modern inertial sensors. The compensation scheme is elaborated in detail: compensation to an arbitrary center of the inertial measurement unit is separately discussed, considering the spreading
effect of the accelerometer triad, and to the center of rotation of the vehicle, accounting for the
installation location on the operational object. Additionally, designs of accelerometer placements
on platforms of high-precision and compact inertial navigation system sensor blocks are analyzed.
By conducting a series of rotations on an inclinable turntable, the spreading of accelerometers is
calculated using the least squares method concerning the intersection point of the rotation axes of
the stand used. An estimation of the discrepancy of the calculated spreading coefficients of sensitive
elements from their nominal values is obtained. Through calibration rotations, the reduction
of all parasitic phenomena in the accelerometer signal due to centripetal and tangential accelerations
is achieved. The influence of parasitic accelerometer signals during the roll of the product on
coordinate computation is analytically derived, revealing the dependency of the studied error on
the product's operational time under constant rolling conditions. Real tests on the inclinable turntable
were conducted for verification, and the obtained results of compensation effectiveness are
presented. The compensation results from flight tests on a two-seat vertical takeoff and landing
helicopter are provided. The flight test calculations were conducted through physical modeling
based on recorded data with the synchronization of the employed sensors considered. Compensation
in the mode of aligning the accelerometer triad to an arbitrary point and aligning accelerometers
to the center of the vehicle's rotation is separately discussed

The integration of several models into one technical vision system will allow solving more
complex tasks. In particular, for mobile robotics and unmanned aerial vehicles (UAVs), the lack of
data sets for various conditions is an urgent problem. In the work, the integration of several models
is proposed as a solution to this problem: segmentation, maintenance and classification. The segmentation
model allows you to select arbitrary objects from frames, which allows it to be used in nondeterministic
and dynamic environments. The classification model allows you to determine the objects necessary for navigation or other use, which are then accompanied by a third model. The paper
describes an algorithm for model aggregation. In addition to models, the key element is the correction
of model predictions, which allows you to segment and accompany various objects reliably
enough. The procedure for correcting model predictions solves the following tasks: adding new objects
to accompany, validating segmented object masks and clarifying the associated masks. The
versatility of this solution is confirmed by working in difficult conditions, for example, underwater
photography or images from UAVs. An experimental study of each of the models was carried out in
an open area and indoors. The data sets used make it possible to assess the applicability of models
for mobile robotics tasks, that is, to identify possible obstacles in the robot's path, for example, a
curb, as well as moving objects such as a person or a car. They demonstrated a sufficiently high
quality of work. For most classes, the indicators exceeded 80% by various metrics. The main errors
are related to the size of the objects. The conducted experiments clearly demonstrate the versatility of
this solution without additional training of models. Additionally, a study of performance on a personal
computer with various input parameters and resolution was conducted. Increasing the number of
models significantly increases the computational load and does not reach real time. Therefore, one of
the directions of further research is to increase the speed of the system

Research in the field of creating specialized computing systems for robots is conducted in
many world scientific centers, including our country. The development of capabilities of sensor systems,
global navigation systems, growth of computing power and improvement of algorithms allow
creating onboard computing systems with broad intellectual capabilities. An important, but unsolved
problem remains in the equipping of such computing systems with domestically produced microprocessors.
An urgent direction in the development of prospective robot control systems is the development
of high-performance on-board computers with the property of survivability. A significant but
unresolved issue remains in the equipping of such computers with computer equipment of domestic
development. The appearance of modern domestic microprocessors Elbrus-2S3 and Elbrus-8SV
opens up new opportunities for robot developers. The emergence of hardware technologies such as a
watchdog timer and a time-binding module makes it possible to create robots with high survivability
in combat conditions. For special purpose robots, it is possible to divide the period of normal operation
of the robot into modes by analogy with the degrees of combat readiness of the armed forces, for
each of which the robot will operate in a special mode. The modes are characterized according to the
prevailing situation and the corresponding failure rate. The paper presents a threat model for the
harshest of the operating modes. This paper presents a method for ensuring the survivability of onboard
robot computers by using adaptive redundancy to ensure the survivability of on-board computers.
The method consists in switching between redundancy schemes to ensure high performance
while maintaining sufficient reliability, depending on the current level of failure flow. Using the model
developed by the authors, an experimental study was conducted to evaluate the effectiveness of the
developed method when working with a domestic onboard computer based on the Elbrus microprocessor.
Using the developed method made it possible to increase the average functionality of the
robot by 23-43% compared to the mode with constant redundancy.

One of the current trends in creating early object detection systems is the development of algorithms
for searching and recognizing small objects in images. In the early detection task, it is necessary
to recognize objects at long distances from the place where they were recorded by the camera.
The image in the image of such objects is represented by a small compact group of pixels, which
undergoes spatial and brightness changes from frame to frame. To successfully solve this problem,
real-world target objects must have large physical dimensions. In addition to the physical dimensions
of the object, the image of the object in the image is influenced by a large number of factors: the resolution
of the camera matrix, the focal length of the lens, the photosensitivity of the matrix, etc. The
vector for solving this problem is directed towards convolutional neural networks. However, even
advanced convolutional neural network architectures face challenges in finding and recognizing
small objects in images. This problem is directly related to the effect of overtraining the neural network
model. Retraining of a neural network model can be assessed based on learning curve analysis.
To reduce the likelihood of overfitting, special methods are used, which are united by the term regularization.
However, in recognizing small-sized objects, existing regularization methods are not
enough. The work examines the developed algorithm for preprocessing a sequence of video frames,
which increases the original feature space with a new independent feature of movement in the frame.
The preprocessing algorithm is based on spatiotemporal filtering of a sequence of video frames, the
application of which extends to a wide range of convolutional neural network architectures. To study
the characteristics of accuracy and recognition of convolutional neural networks, datasets of
grayscale images and images with a sign of motion were generated based on the 3D graphics development
environment Unreal Engine 5. The work presents a criterion for the small size of objects in
images. The accuracy characteristics of the test model of the convolutional neural network were
trained and assessed, and the dynamics of the learning curves of the test model were analyzed. The
positive influence of the proposed algorithm for preprocessing a sequence of video frames on the
integral accuracy of detection of small-sized objects is shown.

The article is devoted to the study of the influence of using various mathematical models of cameras,
and therefore models of scene image formation, when restoring the 3-D structure of a scene from a
set of 2-D images during camera movement (restoring the structure from motion, hereinafter referred to
as LEDs). A comparative assessment is carried out for two camera models: the classic central projection
camera model and the relatively new omnidirectional camera model. The article provides a brief
description of the mathematical model of an omnidirectional camera, the described model is used during
experiments, and also describes ways to represent images from omnidirectional cameras. Additionally,
a description of the mathematical model of the classical camera of the central projection is given.
The described model is also used during experiments. The analytical calculations used in solving the
problem of restoring structure from motion are briefly mentioned in the article. An algorithm for obtaining
3-D coordinates of the points of the observed scene from a sequence of images in motion is also
described. The experiments carried out as part of the study are described in detail in this article. The
process of setting visual landmarks and determining their true 3-D coordinates is revealed. The steps for
the formation of data sets for obtaining comparative estimates are described. At the end of the work, an
analysis of the experimental results is given, models are identified that reduce the errors in restoring the
3-D coordinates of the observed visual landmarks

Modern robots are capable of performing increasingly complex tasks that usually require a
high degree of interaction with the environment in which they have to work. As a result, robotic
systems must have deep and specific knowledge about their workspaces, which go far beyond the
simple representation of indicators that a robotic system can create using visual data processing
techniques, for example, in the task of simultaneous localization and mapping (SLAM). Scene
analysis is the link between object recognition and knowledge about the world around us and is
present in one form or another in the process of extracting information from visual data necessary
to solve a specific task. The article presents a systematic approach to providing on-board STZ
analysis of the scene. The technologies of scene analysis are considered as an integral part of
increasing the degree of autonomy of mobile RTCs. A number of technologies have yet to be mastered
and implemented, but the overall structure allows you to gradually deepen the analysis of the
scene on board the RTK, thereby increasing the degree of autonomy without radically redesigning
the on-board information management system and STZ, as a key part of information support. The
information extracted from the visual data is integrated into a multi-layered map, providing a
high-level representation of the environment, which embodies the knowledge necessary for a robotic
complex to actually perform complex tasks. A multi-layered map is a form of storing
knowledge about the environment and the objects in it. This map combines a spatial hierarchy of
objects and places with a semantic hierarchy of concepts and relationships. The structures for
representing data in various layers of this map and the mechanisms for their use are described. In
particular, to describe the routes of the RTK, the principles of interpretive navigation are used to
provide information about the operating conditions and objects of interest of the signature structure.
The software implementation of the proposed mechanisms is based on a unified approach
based on the real-time STZ software framework. Examples of the use of the described technologies
in solving the problems of information support for targeted movements of ground RTCs are given