No. 1 (2022)

The expediency of creating a passive multi-position radar based on a grouping of unmanned
aerial vehicles is substantiated. The variant of building of such radar is proposed, the main technical
problems of the sonar developing are evaluated and possible ways to overcome them areconsidered. It is shown that for detecting aerial targets and determining their coordinates from
the radio emission of on-board equipment, the difference-rangefinder method is the most promising
as it does not depend on signal modulation and is potentially resistant to interference. For
small-sized UAV for transmitting information over open radio channels, the typical frequency
ranges are 2.4 and 5.0 GHz. A block diagram of a passive multi-position radar has been developed,
including digital shapers of the quadrature components of the received signal, blocks for
detecting and determining the coordinates of the target. The main parameters are calculated and
analytical expressions of digital signal processing algorithms for detecting and determining the
coordinates of the target are given. A stroboscopic effect is used in the digital quadrature component
shaper, which allows for bandpass signals to select the sampling frequency not by the upper
boundary frequency of the spectrum, but by its width, which significantly reduces the requirements
for the performance of the ADC and the DSP devices following it. The complex envelopes of the
detected signals are generated by the method of digital generation in the time domain using digital
low-frequency filters. The detection of signals is performed by an energy detector, the advantages
of which are simplicity of implementation and operability in the absence of a priori information
about the received signal. To determine the coordinates of the radio source, signal delays are
calculated between pairs of signals received by three UAV from a multi-position radar, which are
determined by the maximum modulo values of the mutual correlation functions of the signals in
these pairs. It is shown that the proposed algorithms are well adapted to the processing of possible
sources of radio emission on board small-sized UAV. It is established that the required performance
of the radar computer for real-time operation does not exceed 84.62 GFLOPS. The design
of an on-board antenna module of a passive multi-position radar in the form of a microstrip reconfigurable
antenna, tunable in frequency and polarization, is proposed.

Based on the study of the experience of the armed conflict in Karabakh, some main trends in
the development of tactics for the use of unmanned aerial vehicles (UAVs) have been identified
and possible counteraction options have been identified in the interests of increasing the survivability
of tactical units. The analysis of the state of the issue of the development of modern means of
air defense in the tasks of combating UAVs, including low-speed and small-sized, capable of active
maneuvering and used by groups. Contradictions have been revealed in the requirements for the
early development of specialized means (complexes) of countering UAVs and the real, long timefor their development (5–15 years), as well as the necessary costs for the creation, serial production,
purchase, and delivery of promising specialized complexes to the troops. To solve the tactical
tasks of covering the strongholds of individual units performing tasks in isolation from the main
forces from UAV attacks, the use of available engineering ammunition – directed fragmentation
mines MON-90, MON-200 is proposed. A simulation of the attack situation of small-sized lowflying
UAVs was carried out, and the probability of hitting UAVs with engineering fragmentation
mines was calculated, which showed an acceptable probability of hitting low-flying UAVs when
they were in the sector of detonation and scattering of a cloud of damaging elements of mines.

The paper proposes the use of marine mobile robots to counter mining, detection, classification
and localization of mines. In accordance with the purpose, search and reconnaissance devices
have been identified for the survey of water areas that are capable of operating autonomously or
in remote control mode with decision-making support. The analysis of existing types of sensor
systems for the survey of water areas is carried out. The main results of theoretical and experimental
studies of ways to improve magnetometric sensor systems of marine robots are presented.
It is proposed, based on the criterion of ensuring the greatest capabilities of marine robots for
detecting and the pace of searching in offline mode with known weight and size limitations, the
construction of a magnetometric sensor system with automated recognition of explosive objects.
For the purposes of automated classification of search objects, it is proposed to take advantage of
neural networks, which, unlike traditional machine learning, provide the possibility of high-level
abstract expression of the semantics of internal connections between data by choosing architectural
solutions. The structure of the neural network is obtained based on the linear classification
of explosive objects according to two parameters of the training sample. Based on a proven training
sample and a classifying function by two parameters, for a multichannel magnetometric system,
an implementation of the neural network structure has been developed that takes into account,
in addition to the ferromagnetic mass and depth of occurrence, the parameters of the geometric
shape of real explosive objects. The directions of improving and increasing the range of
ferrosonde sensors as the most suitable for the construction of magnetometric detection systems
for marine mobile robots are determined. A method is proposed to increase the sensitivity of
ferrosonde magnetometric sensors of marine robots through the use of new magnetic materials
and circuit solutions. To create highly sensitive ferrosonde magnetometric sensors, the use of
cores made of amorphous cobalt-based alloys of the AMAG-170 type is proposed, providing a
potential opportunity to increase the conversion coefficient (sensitivity) of the sensor system by
increasing the excitation frequency of the core of the ferrosonde. The functional diagram of the
layout of the developed magnetometric ferrosonde sensor system based on two rod cores made of
amorphous alloy AMAG–170 is presented.

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

The use of a wide range of radio stations of various generations in the Armed Forces of the Russian
Federation has led to the impossibility of fully taking into account all their features when planning
radio communications. For example, it becomes an extremely difficult task to take into account the entire
list of self-detected frequencies by the emissions of their own heterodynes of various radio stations,
as well as frequencies prohibited for establishing and conducting radio communications. In this regard,
the risks of lack of radio communication or violation of the rules for the use of the radio frequency range
have increased many times, including in the units of military robotic complexes (RTK VN). The article
notes that one of the biggest problems in creating a unified digital combat control system, the technical
basis of which will be a wireless communication network - a radio communication network operating in
various frequency ranges, today is meeting the requirements of electromagnetic compatibility (EMC)
and, in particular, meeting the requirements for the correct use of the radio frequency spectrum.
To optimize the process of radio communication planning in the formations of the RTK VN, to increase
its efficiency and effectiveness, the article proposes to create and apply an automated system for monitoring
the assignment of radio frequencies based on a risk-based approach. It is shown that the riskoriented
approach when planning radio communications in the divisions of the RTK VN involves a
comparative assessment of the nominal value of the assigned operating frequency with a list of selfdetected
frequencies of each of the radio stations used, as well as with a list of prohibited frequencies for
establishing radio communications (frequencies for transmitting distress signals, etc.). It is reasoned
that this approach will reduce the risks of lack of radio communication or violation of the rules for using
the radio frequency range. It will optimize the processes of automated planning of radio communications
in the formations of the RTK VN and increase the efficiency and effectiveness of automated planning
and management of radio communications during combat operations. The article describes the
algorithm of the automated control system for the assignment of radio frequencies. The software implementation
of this algorithm involves the creation and storage in the appropriate memory area of libraries
(databases) of the main prohibited frequencies defined by the legislation of the Russian Federation.
In addition, these libraries (databases) store lists of self-reflected frequencies by the emissions of their
own heterodynes of all types of radio stations used, given in the technical description of each of the
radio stations used.

In order to create an innovative module for automatic correction of algorithms for automatic
detection and tracking of objects with real-time training, a study of world experience in the field
of general-purpose automatic tracking with the ability to recognize the tracking object for use in
embedded computing devices of optoelectronic systems of promising robotic complexes was carried
out. Based on the conducted research, methods and approaches have been selected and tested
that allow with the greatest accuracy, while maintaining high computational efficiency, to provide
on-the-fly training of classifiers (online learning) without a priori knowledge of the type of tracking object and to ensure subsequent correction during tracking and detection of the original object
in case of its short-term loss. Such methods include a histogram of directional gradients – a descriptor
of key features based on the analysis of the distribution of brightness gradients of an object
image. Its use allows you to reduce the amount of information used without losing key data
about the object and increase the speed of image processing. The article substantiates the choice
of one of the classification algorithms in real time, which allows solving the problem of binary
classification - the method of support vectors. Due to the high speed of data processing and the
need for a small amount of initial training data to build a separating hyperplane, on the basis of
which the classification of objects takes place, this method is chosen as the most suitable for solving
the task. To implement online training, a modification of the support vector machine was chosen,
implementing stochastic gradient descent at each step of the algorithm – Pegasos. Another
auxiliary method is the clustering method of key points – this ensures an accelerated selection of
objects for classification and training. The authors of the study carried out the development and
semi-natural modeling of the proposed module, evaluated the effectiveness of its work in the tasks
of correcting and detecting the object of interest in real time with preliminary online training in
the process of tracking the object. The developed algorithm has shown high efficiency in solving
the problem. In conclusion, proposals are presented to further improve the accuracy and probability
of detecting an object of interest by the developed algorithm, as well as to improve its performance
by optimizing calculations.

The production of agricultural goods is currently associated with the use of digital technologies,
elements of precision farming, automation and robotization of agriculture. These technologies
make it possible to carry out continuous monitoring, carry out timely processing, improve the
efficiency of production and use of resources. The need for the integrated use of digital technologies
and artificial intelligence and the creation of intelligent integrated systems for agricultural
production is noted. Studies show that IT-technologies are actively used in field farming when
growing grain crops. The main crop in the production of breeding, seed and commercial grain in
the Kabardino-Balkarian Republic is corn, so it is assumed that the intelligent system of the "smart
field" should be developed initially for this particular crop, and then, with some modifications,
used for the production of any crop products – other types of grain, vegetables, fruits, grapes and
gourds. It allows you to reduce human participation at some stages of production by automating
the process and controlling it through various "smart" devices. The operation of the "smart field"
system is based on the use of a variety of sensors, including those installed on mobile equipment
(ground and air manned and unmanned vehicles, space satellites) and portable portable devices to
obtain operational data on the state of fields and crops. This allows: – analyze the readiness of
agricultural land for sowing, monitor the progress of plant vegetation in order to effectively and
efficiently plan agrotechnical measures (chemical protection against pests and diseases, fertilizing,
irrigation, etc.); – predict production efficiency indicators (total gross harvest, yield per hectare), as well as timely identify production risks (appearance of pests, plant diseases, soil salinity,
etc.). – make effective decisions on managing the use of resources of agricultural enterprises. With
the use of "smart" devices, it became possible to introduce the so-called. "precision farming" to
manage crop productivity, taking into account changes in the plant habitat. Ultimately, this makes
it possible to solve two main tasks of agricultural producers - increasing yields and reducing
costs. The authors have developed the concept of an intelligent integrated system "Smart Field" for
the production of corn grain using advanced robotic systems and complexes. The architecture of
the "Smart Field" system for the production of seed and commercial corn is presented, which can
be adapted with minor modifications for the production of other crop products.

The weather affects the productivity and condition of crops, the requirements for the quantity
and quality of fertilizers, as well as preventive measures to prevent diseases. Bad weather can
affect the quality of products during transportation and storage, and hence the germination of
seeds and planting material. Various intelligent monitoring systems are now widely used in agriculture,
which include satellite monitoring and weather stations. In this case, the choice of a
method for analyzing the received data and intelligent systems for their processing for predictive
forecasting plays a fundamental role. The purpose of this study is to develop an intellectual system
for predicting the state of the crop based on data from a weather station. A multi-agent algorithm
for predicting the state of crops according to data from a weather station based on the selforganization
of neurocognitive architecture was developed in this study. The description of the
block diagram of the weather station and its sensors is given. A program algorithm has been developed
for collecting and processing data from weather station sensors. As a result of processing,
data on air and soil temperature, air and soil humidity, wind speed and direction, precipitation
amount and the sum of active temperatures are sent to the intelligent decision-making system. A
system for constructing cause-and-effect relationships is described. This system can make recommendations
or forecasts on the condition of the crop and on the likelihood of diseases and pests in
controlled crops.

The aim of the study is to construct a suboptimal controller with the -criterion that stabilizes
the deviation of the dynamic system from the given program trajectory. It is assumed that an impulse
disturbance will be applied to one input of the system, and an -disturbance to the second one.
The -norm is equal to the maximum value of the -output norm for all - and impulse disturbances
vectors for which the sum of the quadratic form of the impulse disturbance vector with a given
weight matrix and the squared -norm of the second disturbance never greater than one. In this paper,
it is required to demonstrate the process of calculating the -norm in terms of linear matrix inequalities
for a dynamical system and a system with uncertainty. An important role in the process of
combining the -norm and the -norm in the -norm is played by the weight matrix included
in the definition of this norm. It should be noted that, unlike the -norm, the -norm is achieved
in the sense of the worst - and impulse disturbances, where the maximum value of the -output norm
is reached. It is necessary to obtain and linearize the mathematical model of the quadcopter, build a
programmed trajectory, and stabilize the deviations using a suboptimal control law with the -
criterion in the presence of noise in the system. Linear matrix inequalities are used for suboptimal control
searching. The object of this study is a quadcopter, which is an unmanned aerial vehicle with four
engines with propellers that create thrust. The axes of the propellers and the angles of the blades are
fixed and only the speed of rotation is regulated, which greatly simplifies the design. Using the Newton-
Euler equation, a nonlinear mathematical model of a quadcopter is obtained, and this model is linearized.
In the MATLAB environment, using the applied package for modeling and optimization YALMIP,
Sedumi toolbox, numerical modeling, and construction of quadcopter motion trajectories are performed.
After that, in the Simulink environment, a control block that stabilizes the movement of the
quadcopter along a given trajectory in the presence of - and impulse disturbances in the system is
constructed. At the end, a demonstration of the process of virtual visualization of the flight is made.

The relevance of the development of algorithms for managing a group of UAVs in the event of
crisis situations that affect the performance of the task is substantiated. An algorithm for autonomous
collective (decentralized) control of a group of UAVs is described when performing the target task of
transporting goods, as well as combined control in the event of crisis situations when the autonomouscontrol mode cannot be fully implemented. The algorithm for working out a crisis situation in case of a
lack of energy resources on board the UAV and the return of group agents to the starting position is
described in detail. The results of modeling the movement of a group of UAVs of multirotor and aircraft
types and working out a crisis situation for managing a group of UAVs based on information about the
reserves of energy or fuel resources are presented. During the experiment, iteratively calculated the
remaining fuel when the UAV moved to the landing point, as well as the amount of fuel available to the
UAV at a given time. As a result of the experiments, it was found that the time for calculating the balance
of the energy resource does not exceed 6.792 ms. If the leader runs out of fuel, the cargo transportation
mission ends ahead of schedule, since it cannot be completed without the participation of the
leader. If several slaves fail, the mission can be continued if their number does not exceed a predetermined
value, which is critical for the continuation of the cargo delivery mission. The results of experimental
studies on modeling the flight of an UAV with a load are presented, during which a flight route
was built that simulates a curvilinear trajectory of movement in urban conditions from the starting point
to the end point, where the UAV is landing and transferring the cargo. In the experiments, the developed
UAV and the onboard fastening system of the thermal container were used. During flight tests, the average
horizontal speed of the UAV was set to 10 m/s. The length of the flight was 5350 m. The flight time
was 13 minutes. 51 seconds.

The development of robotics makes their group application relevant for solving various
tasks. The effectiveness of performing the tasks of detecting and determining the coordinates of
objects by a group of robots significantly depends on the accuracy of maintaining a given formation.
In this regard, the task of determining motion planning algorithms that ensure the greatest
accuracy of maintaining a given formation is of practical interest. This article is devoted to the
study of the accuracy of maintaining the formation of a multicopter-type UAV group using a centralized
motion planning algorithm and a decentralized algorithm. The centralized algorithm uses
a master UAV, which transmits its coordinates to the slave UAVs. Based on the coordinates obtained
and the given framework of the formation, the guided UAVs plan their movement. In a decentralized
system, neighboring UAV groups transmit their coordinates to each other, on the basis
of which the movement of a separate UAV is planned. The accuracy of the control system is investigated
depending on the errors of the navigation system and the frequency of updating data on the
position of the leading or neighboring UAVs. It is assumed that the group's UAVs determine their coordinates in discrete moments of time using an external navigation system. Centralized and
decentralized algorithms are worked out by the same motion control system. The algorithms are
investigated in this article by numerical modeling methods. In the process of simulation, models of
kinematics, dynamics and actuators are taken into account, as well as models for the formation of
errors in the navigation system. It is shown that the de-centralized algorithm of group motion
planning provides higher accuracy compared to the centralized algorithm. However, the technical
implementation of a decentralized algorithm is more complicated from the point of view of organizing
a group communication system. In a centralized system, data transmission from the master
UAV to the slave should be implemented. In a decentralized system, it is required to implement
network communication.

The article presents a numerical simulation of non-stationary convective-conductive heat
transfer of the strapdown inertial navigation system (SINS), developed in the JSC «CNIIAG».
The numerical simulation is carried out in the ANSYS Mechanical. The aim of the study is a comprehensive
analysis of heat exchange processes, which are characteristic to the device operation,
including mutual spatial influence of thermal powers on each other, as well as on the block of
sensitive elements. The simulation of heat transfer inside the hermetic case of the SINS is carried out for critical operating conditions in a strongly and weakly coupled consideration with a comparison
of both approaches. ANSYS Mechanical, CFX and System Coupling simulation modules
are chosen for program implementation of each approach. The k-e model of air turbulence with
implicit consideration of the effect in the boundary layers and diffusion correction in shear flows is
chosen for this approach. External heat exchange with ambient air is considered by setting convective
boundary conditions on the external surfaces of the SINS, considering their orientation.
To obtain numerical values of the heat transfer coefficients, the orientation of each surface in
space is taken into account by using the appropriate coefficient. The presence of irregularities on
the surfaces of the SINS in the contacts between solid components is considered by using the calculation
of thermal resistances of the actual contact and intercontact layer. The simulation results
of deformed state of SINS structural system, resulting from the action of a non-symmetric thermal
field, is presented. The analysis of the obtained graphs is carried out. Stiffness indicators of the
SINS structural system is defined as angles of deviation of sensitivity axes caused by thermal deformations.
The obtained results make it possible to evaluate the engineering solutions for the
quality of heat removal from the elements of the PCBs, bypassing the sensitive elements of the
device, adopted at the stage of product layout.

One of the obligatory requirements for parallel mechanisms design is the exclusion from the
workspace of singularities in which the mechanism loses its controllability and malfunctions may
occur. The analysis of the workspace of the mechanisms of a parallel structure is more complicated
than that for the mechanisms of a serial structure, especially if the mechanism has more than
three degrees of freedom. The article considers the problem of analyzing the influence of singularities
on the solution of the forward kinematics and the geometry of the workspace 3/6 of the
Gough-Stewart platform (commercial name - "Hexapod"). A numerical algorithm for solving the
forward kinematics of platform has been developed. It is based on the direct use of the system of
equations of the platform's kinematic constraints. Approximation of the set of solutions to the system
of equations is based on deterministic methods of global optimization. An analysis of the
change in the number of forward kinematics near the zone of singularities is performed. The analysis
consists of two stages. The first stage consists in solving the forward kinematics for the position
and orientation of the platform, at which singularities arises. The second stage consists in
solving the forward kinematics for the case of a singularity and the case near a singularity.
As a result of solving the forward kinematics, a different number of forward kinematics solutions
for different cases was revealed. An algorithm has been synthesized that makes it possible to determine
a singularity-free workspace free for given ranges of change in the platform orientation
angles specified by Euler angles. An analysis of the dependence of the change in the volume of the
workspace depending on the range of change in the angles of the platform orientation was carried
out. The algorithms are implemented programmatically in the C++ programming language.
The modeling was performed using parallel computing and the implementation of the export of
three-dimensional models of the positions of the platform and workspace to the universal format of
three-dimensional models STL.

The work is devoted to the problem of diagnosing the internal combustion engine of vehicles
this problem is now the most relevant due to the constant growth of the car fleet and the tightening
of requirements for safe operation. Timely and accurate control of the internal combustion engine
is able to prevent the failure of entire vehicle assemblies, as well as to avoid such serious consequences
as a traffic accident. With the advent of modern technologies the long-known method of
engine condition estimation by sound can become the most advanced, since the human factor is
excluded, for signal processing the computer technique is applied, the analysis of a sound spectrum
in which is carried out by means of artificial neural networks. The application of artificial
neural networks for analyzing the sound spectrum has found application in speech recognition and
for diagnosing diseases of the respiratory system. The article deals with the failure of one of the
main parts of internal combustion engine - the bearing. All possible types of bearing faults and the
reasons why they occur are presented. The nodes and mechanisms of the internal combustion engine
in which bearings are used are listed. The algorithm of the experimental part is described.
The experiment which includes transformation of the received sound signals into spectrograms
and extraction of features with the help of which the classification is carried out, is executed. The
executed experimental part has proved the possibility of diagnosing of the internal combustion
engine by means of artificial neural networks. Scientific novelty lies in the fact that the diagnostic
process becomes automated, all the sounds taken by sensors are processed in a computer or in the
future in a special scanner, the display shows information about the state of certain nodes, unlike
traditional methods where the diagnosis is carried out visually or by ear. Thus, the diagnostic
accuracy increases and the overall labor intensity decreases due to the exclusion of partial or
complete engine disassembly.

Model Predictive Control is an advanced process control method that used while meeting a
set of constraints. From an engineering point of view, the MPC method of designing control systems
is attractive, because is relatively simple in design, including for solving complex production
problems. This method is similar to the classical synthesis of a control system based on a linearquadratic
controller (LQR). The key difference between MPC and LQR is that predictive control
solves the optimization problem within a sliding time horizon, while the linear quadratic method
used to solve the same problem over a fixed time window. The paper considers a method for constructing
two-wheeled mobile robot control system using Model Predictive Control. The process of
building a mathematical model of the mechanical system of the robot is given, as well as the linearization
of the resulting model is performed. The basic principles of constructing a control system
based on MPC for linear systems without external disturbances, as well as using an observer to
assess the state of the model under the influence of additive white Gaussian noises, are presented.
A variant of the synthesis of a control system with imposed restrictions on the input signal is considered.
Also presented is a method for determining the position of a two-wheeled robot in space
using a vision system, which is based on the use of a neural network. The architecture of the used
model is given, as well as a stereo camera, which used to build an image depth map. In addition to
the above, the work describes in detail the principle of the deep learning model – YOLOv3, which
based on several blocks of input data processing. A detailed description of the implementation of a
stereo camera in conjunction with an artificial neural network model using the Python programming
language and libraries for working with video data and a stereo camera is presented.

The purpose of the study is to substantiate the effectiveness of the use of various types of
mounted and removable working equipment of robotic complexes based on autonomous platforms for
emergency rescue operations in the emergency zone. The research carried out within the framework
of the creation of the experimental robotic platform "Marker" allowed us to begin the development of
emergency rescue mobile robotic complexes capable of performing tasks in an emergency zone inautonomous mode. It is proposed, based on the analysis of tasks performed in the emergency zone, as
well as a list of special equipment of machines with weight and size indicators close to those of the
Marker platform, to evaluate the optimal set of mounted and removable working equipment that ensures
the performance of tasks with minimal time costs. During the research, an approach was proposed
that allows solving similar tasks for any robotic platforms, regardless of the base chassis used.
Also, the proposed approach made it possible to determine the main areas of improvement of the
autonomous robotic platform "Marker" in order to ensure the possibility of performing emergency
rescue operations in the emergency zone. These areas include the development of a hydraulic system
that allows you to control mounted and removable equipment, both when performing technological
operations and when moving around the emergency zone, and performing manipulations related to
their replacement, including in automatic mode. As a second direction, the need to refine the design
by strengthening the attachment points of attachments and working equipment, as well as the main
load-bearing elements that will be involved in performing technological operations using a calculated
set of attachments and removable equipment was determined. The authors consider the third direction
to be the need to develop algorithms and software for controlling an autonomous robotic
platform when performing technological operations in an emergency zone, including earthmoving
operations, loading and unloading operations, search and rescue operations, as well as actions related
to monitoring the situation in the area of emergency rescue operations.

The paper exams the problem of identifying faults in the drives of robotic systems, the dynamics
model of which is described by linear differential equations. It is proposed to search for a
solution to the fault identification problem based on the solution of an auxiliary optimal control
problem for a dynamical system in which the role of an unknown vector function describing
emerging faults is performed by some auxiliary control, which should provide a minimum for the
residual functional. Based on the solution of the auxiliary optimal control problem, a fault diagnostic
observer is proposed. In this case, the fault itself is found through the solution of the corresponding
algebraic Riccati equation and the differential equation for the auxiliary variable. Unlike
popular approaches to solving the problem of fault identification based on observers operating
in a sliding mode, the proposed method allows us to expand the class of systems for which the
identification problem can be solved. It is known that the methods of sliding mode observers design
impose certain restrictions on the systems under consideration. The proposed approach based
on optimal control can also give results for systems with nonlinear dynamics. In this case, methods
of approximate solution of optimal control problems based on the representation of the system in
linear form with state-dependent coefficients (the so-called State-dependent Riccati Equation,
SDRE) are likely to be effective. The improvement of the proposed method in this direction will be
the subject of further research. The stated theory is shown on the example of fault identification in
a DC drive. Different cases are considered for a system with complete observations (the entire
state vector is known) and with incomplete observations. It was shown during the simulation that
the quality of faults identification can be improved by selecting the appropriate values of the penalty
matrices in the residual functional, while it is possible to achieve good diagnostics separately
through various channels of faults occurrence. The paper presents recommendations on the choice
of penalty matrices. The simulation results confirmed the operability of the diagnostic observers
synthesized using the proposed method.

The aim of the study is to develop a non-contact fluxgate position sensor to control the open
/ closed state of the valve. There are many examples of the use in modern technology of elements
or devices that interact with a magnetic field. One of the most urgent tasks is to use the influence
of the magnetic field as a means of control or as a component of the control environment. The use
of magneto-optical sensors for monitoring the functioning of technical objects is due to their noncontact
measurement method, the ability to measure not only magnetic, but also various other
physical quantities, the relative simplicity, reliability and low cost of the design of the sensitive
element, flexibility in use, operation in low-temperature and high-temperature environments.
One of the sensors of this type is a fluxgate magnetic field converter. Valves of various
pneumohydraulic systems are an example of the object of introduction of a fluxgate sensor.
The essence of the task is to create a non-contact limit switch of the valve spool, signaling the
closed or open state of the valve and transmitting this information to the control system. It is proposed
to divide this task into stages and their sequential implementation. First, a search and analysis
of existing solutions that implement the position sensor using the fluxgate control method is
carried out to improve the design being developed. Next, the initial design of the sensitive element
of the fluxgate transducer is developed, according to the initial design, a geometric 3D model of
the sensitive element is created, and the proposed material of the constituent elements of the sensor
is selected. With the help of numerical methods of computer simulation, the operation of the
sensor is simulated and its output characteristic is determined under various operating modes.
According to the design characteristics, the optimal design and configuration of the sensor's sensing
element is selected and calculated. As a result of the simulation, assembly and working drawings
of the sensor are developed. The proposed method for solving the problem is characterized by
the complexity of studying nonlinear magnetic systems and their modeling. The results of this study
can be recommended for the development of magneto-optical sensors of this or another type and
for the study of materials with nonlinear magnetic properties.

The aim of the work is to create a productive computing device for a strapdown inertial navigation
system (SINS) of a ground-based robotic complex (RTC) on a domestic element base.
A formal description of the typical sufficient functions performed by SINS is given and the basicprinciples of the algorithms are described from the point of view of the requirements for computing
resources. A description of domestic microcontrollers available on the market and a comparison with
the closest foreign analogue are given. The results of the prototyping carried out showed the fundamental
possibility, but the low prospects of creating computing devices on a single microcontroller.
In this regard, technical proposals were developed and implemented to increase the computing power
by means of building the architecture of a multiprocessor computer. As a result, it was necessary
to develop special approaches to the design of algorithms and software. The organization of distributed
computing is one of the most optimal methods for ensuring the calculation of functioning algorithms.
The introduction of additional microprocessors into the calculator circuit made it possible not
only to increase the computing power, but also to introduce additional interfaces for interaction with
both the consumer and primary information sensors. The proposed variant of the distribution of SINS
operation algorithms made it possible to create a reserve for the development prospects and system
scalability. The most resource-intensive algorithm is the calculation of inertial coordinates, implemented
as an iterative calculation for determining the latitude component of the location. Also, the
performance margin may allow the implementation of additional adaptive algorithms for filtering
and processing data based on the results of testing and operation of a ground moving object.
The choice of on-board exchange interface between controllers is substantiated and its practical
application is described. The creation of a closed loop of information exchange made it possible to
implement additional parallel calculations of secondary information and to calculate an autonomous
reckoning of the object's location coordinates. The described technical solutions can be used in the
design of embedded calculators for objects for various purposes operating on the basis of hard logic.
As the main drawback of the presented approach to designing a calculator, one can designate a limited
functionality when working with ROMs.

Satellite radio navigation systems make it possible to evaluate the user's state vector, use
coordinates, user speed and time relative to the system scale. The requirements for the characteristics
of these systems constantly depend on the fact that they have application features in their
algorithms for processing radio navigation signals. One of the main characteristics of satellite
radio navigation systems is the accuracy of estimating the user's state vector. This characteristic
can be improved by the presence of estimates of the phase of the received radio navigation signals.
In a satellite radio navigation system, phase estimation errors in the tracking loop have two components:
dynamic and noise. To compensate for the noise error, it is necessary to reduce the
equivalent noise band of the anti-aliasing filter of the phase tracking loop. However, the minimumpossible bandwidth of the smoothing filter is limited by the presence of consumer dynamics and the
quality of the reference oscillator. As a result, in the presence of consumer dynamics, the sensitivity
and reliability of phase tracking deteriorates. To compensate for the dynamic error in the phase
tracking loop, information from an inertial navigation system can be used. The satellite radio navigation
system and the inertial navigation system have complementary characteristics. The use of
support for phase tracking loops from an inertial navigation system makes it possible to increase
the sensitivity and reliability of its operation in the presence of consumer dynamics. It is assumed
that with such an implementation, the sensitivity of the phase tracking loops will be limited only by
the instability of the reference oscillator and the error of inertial measurements. To improve the
characteristics of accuracy, sensitivity and reliability of the coherent mode of operation of the end
device, an algorithm was developed to support phase tracking loops with measurements from an
inertial navigation system. A study of the developed algorithm was carried out on a model that
uses real measurements of satellite and inertial navigation systems as input data. The developed
algorithm is implemented in the software of the NV216C-IMU inertial satellite navigation system
prototype. Experimental studies were carried out in the conditions of automobile dynamics in open
areas. The research results are presented in the work.

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 the equipping of such computing systems with domestically produced
microprocessors. The need to take into account the maximum weight and size characteristics, the
requirements for the cooling system of the computing complex, the requirements for on-boardpower supply and power consumption, performance requirements and external interfaces cause
the complexity and high cost of developing on-board computing systems. The lack of unification of
computing modules during development creates additional difficulties for developers of robots,
increases the final cost of the robot and complicates its modernization. The use of a standardized
form factor such as COM-Express makes it possible to divide the onboard computing complex into
a universal high-tech system part and a carrier board. The microprocessor, peripheral interface
controller, RAM and hard disk are placed on the system module, which is produced in large quantities
and can be replaced with a newer one when new generations of domestic computing equipment
appear. The carrier board, in turn, is easy to develop and cheap to manufacture, and according
to its characteristics can be configured for a specific robotic complex. This article discusses
COM-Express modules based on domestic Elbrus microprocessors. Their applicability for the
creation of promising on-board computing systems is shown. The results obtained by the authors
indicate the prospects of import substitution in the field of robotics.

In order to develop a stable algorithm for automatic detection and tracking of nondeterministic
objects with real-time learning for embedded computing systems with optoelectronic
devices, within the framework of this work, a study and analysis of the existing world scientific and
technical experience in the field of automatic tracking algorithms for general purposes was carried
out. The article shows that the most stable modern automatic tracking algorithms are a system
that makes a decision about the current position, size and other parameters of the tracked
image based on the model being trained. The authors of the study identified the most effective of
the applied basic algorithms suitable for use in embedded computing systems of robotic complexes,
and developed a new algorithm for automatic detection and maintenance of non-deterministic
objects. A semi-natural testing of the developed algorithm was carried out and its effectiveness
was evaluated in solving problems not only of automatic tracking of objects, but also problems of
automatic detection of objects using several reference images. In conclusion, proposals are presented
for further improving the accuracy of the developed algorithm and for its optimization and
implementation in the special software of on-board computer systems of aircraft.

Currently, the improvement of the quality of transport and logistics services provided is directly
related to the introduction of new and modernization of existing technologies of
informatization and digitalization. One of the most urgent tasks solved by the introduction of digital
technologies into existing technological processes is to improve the safety of train traffic.
The analysis of domestic and foreign works devoted to the development of train safety improvement
systems has shown that one of the methods of solving the task is the development and implementation
of vision systems for detecting infrastructure objects and obstacles in the course of train
movement. This is especially true when train speeds increase when it is difficult for the driver to
correctly assess the current situation and make an operational decision. This paper describes the
implementation of a vision system for unmanned trains. Within its framework, a new approach to
the training of a highly specialized mask neural network was implemented. The main task of this
system is to recognize obstacles and human figures against the background of the railway infrastructure
determine their location relative to the tracks and assess this situation from the point of
view of traffic safety. To obtain a higher-quality mask, the approach of simultaneous use of images
of standard CVS cameras and cameras with the higher resolution was used. This method is able toimprove the quality of recognition, especially at large distances, when the object of interest is not
noticeable in the complex environment surrounding it. The work performed has shown good results
in identifying objects on railway tracks. The creation of a prototype of such a system and
equipping it with traction rolling stock will allow for the timely detection of obstacles and people
on the train path, which contributes to improving the level of train safety.

The actual problem of determining all six coordinates (three linear and three angular) of the
current position of a mobile robot (unmanned aerial vehicle) from video rangefinder images of the
external environment (volumetric colored point clouds) formed by an onboard integrated vision
system built on the basis of a 3D rangefinder sensor (lidar) and a color video camera while moving
(flying) in an unknown environment is considered. An algorithm of video navigation based on
the use of complexed (video-rangefinder) descriptors is proposed, for the description of which
visual and geometric parameters are used. The rules for the formation of a complex descriptor are
formulated, which ensure the allocation of special (central) points of the descriptor using the
Sobel operator and the calculation of brightness and geometric parameters in its local area. The
addition of the brightness parameters of the descriptor provided by the video camera with the geometric
parameters provided by the rangefinder sensor removes the problem of invariance of the
descriptor to the scale and thereby significantly reduces the complexity of calculations when selecting
it. The rules for finding complexed descriptors corresponding to each other in a sequence
of complexed images are described, based on calculating the difference in brightness and geometric
parameters of the compared descriptors. The estimation of the error in solving the navigation
problem using the integrated descriptors was performed depending on the error of the sensors of
the vision system and the geometric dimensions of the descriptor. By constructing histograms of
the solution of the navigation problem for each coordinate of the control object for all pairs of
descriptors corresponding to each other, a statistically stable high reliability of the solution of the
complete navigation problem has been achieved. At the same time, the error in solving the navigation
task turned out to be an order of magnitude smaller than the error in the formation of complex
images by the technical vision system. The use of complex descriptors made it possible, with a
relatively small amount of calculations, to solve the complete navigation problem with acceptable
accuracy, which provides a solution to the SLAM problem on the onboard computations at the
pace of movement of the control object. The effectiveness of the proposed algorithmic and developed
software and hardware is confirmed by field experiments conducted in real conditions of
various environments.

One of the key areas in the artificial intelligence is technical vision. For resource-intensive
tasks of technical vision high-performance, computing systems are created with use of specialized
accelerators. The use of such accelerators is necessary due to the inability of general-purpose
microprocessors (GPM) to solve such problems in a given time due to a high computational load.
However, the microprocessors of Elbrus series are successfully used to solve technical vision
problems in both server and on-board modes, and the appearance of the sixth-generation Elbrus
microprocessors should further improve performance on such tasks. Due to the high cost, greater
complexity and limitations in the use of systems with specialized accelerators, the question arises
of determining the conditions under which, it is sufficient to use CPU’s to solve the tasks of technical
vision, for example, with the microprocessors of the Elbrus series without special accelerators.
One of the most resource-intensive tasks in the field of technical vision are detection and
classification of objects. For the detection of objects one of the popular methods is the Viola-Jones
method. Convolutional neural networks are usually used to solve the classification problem.Mathematical models of computations have been developed for VGG16 and VGG19 neural networks
in relation to the actual microprocessors of the Elbrus series. Using the developed models,
the theoretical sufficiency of the performance of Elbrus microprocessors for technical vision tasks
is substantiated. Also, based on these methods, programs for modeling detection and classifications
objects in the image and video stream have been developed. The programs are written in
C++ using the OpenCV library, OPO Elbrus, the GNS Platform library and the ImageNet competition
database. Using the implemented programs, comparative testing was carried out on a number
of high-performance computing systems with Elbrus and Intel CPU’s and NVidia video card.
Based on the results obtained, it is shown that the Elbrus-8S is sufficient to solve the problem of
searching for objects in the image for input resolutions up to 1920 x 1080, where the processing
speed of the video stream is more than 20 frames per second.

The work is devoted to the optimization of the neural network architecture for its launch on
a limited computing resource. Several optimization approaches are considered, estimates of the
complexity and execution time of the forward pass of the neural network are given. Comparative
estimates of the complexity of the network using different optimization approaches are given.
The paper presents an analysis of the selected network architecture, and estimates of the computational
complexity of individual components (modules) of the architecture are obtained. An analysis
of possible optimization methods for each module was made. The parameters of the considered
modules, the sizes of the input and output tensors are described. Several architectures were tested
to optimize the feature extraction module, ResNet 50, ResNet 18, MobileNet v3 small, MobileNet
v3 large. A comparative analysis of the computational complexity and execution time of the forward
pass for each architecture is presented. Forward pass times were measured on Nvidia's
Jetson AGX Xaver embedded computing device. Estimates of the execution time of the direct pass
for each module of the considered neural networks are presented. The paper presents the results of
comparing neural network accuracy estimates before and after architecture optimization. The test
data set consists of 100 video recordings. 5 different typical objects are involved in test videos,
10 different scenarios are recorded for each object class. For each of the developed architectures,
accuracy estimates were obtained, and a comparative analysis was made.