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• DEVELOPMENT OF HEURISTIC ALGORITHMS FOR OPTIMIZING THE LOCAL TRACTORY OF UAVS BASED ON OBSTACLE AVOIDANCE PATTERNS

  L.А. Rybak , I.А. Duen , V.V. Cherkasov , А.А. Voloshkin , Т.А. Dunin

  2026-04-29

  Abstract ▼

  A key challenge in developing an information and control system for autonomous navigation of unmanned aerial vehicles (UAVs) in the absence of satellite communications is the generation of a local trajectory in the presence of obstacles (trees, power lines, etc.). The goal of this study is to develop heuristic algorithms that optimize the UAV's local trajectory using LiDAR data and construct a feasible local trajectory based on obstacle avoidance patterns. A two-stage concept is proposed: decomposing the LiDAR point cloud into oriented bounding boxes (OBBs) and generating a trajectory for traversing the OBBs using geometric patterns. The first stage implements a classic (geometric) LiDAR data processing pipeline: voxel thinning, ground plane extraction using the RANSAC method, DBSCAN clustering, and constructing bounding boxes around the clusters. This approach is implemented as a Python software module. Simulations were performed for two scenarios. The first scenario contained three obstacles, one of which was isolated, while the second and third were located in a group. The generated trajectory avoided all obstacles, with a trajectory construction time of 0.29 milliseconds. The second scenario was performed for a set of obstructions obtained by point cloud decomposition; the total number of obstacles, including the ground, was 678. [This is a fragment of the original text. The trajectory construction time in this case was 0.377 seconds. This approach provides predictable performance and a linear computational complexity estimate based on the number of obstacles, making it promising for use in autonomous navigation and UAV motion control systems

• MODELS OF SEAMLESS OPERATION OF A GROUP OF AGRICULTURAL UAVS

  А.I. Saveliev , А.V. Ryabinov , А.V. Semenov

  2026-04-29

  Abstract ▼

  The introduction of groups of unmanned aerial vehicles (UAVs) into precision farming is hampered by several problems related to the stability of communications in areas remote from the take-off point and dependence on weather conditions, which can reduce the effectiveness of the technology. Existing planning models are insufficiently adaptive to dynamic changes in the agricultural environment, communication problems, and in most cases assume strict adherence to fixed trajectories. The purpose of this study is to develop conceptual models of seamless operation and a communication system for coordinating a group of agricultural UAVs. The paper presents a diagram of the connectivity of the system elements, which ensures the continuity of processes from initialization to automatic battery replacement and UAV refueling. A communication model of the system with the relay node has been developed that separates traffic, which makes it possible to eliminate collisions and stabilize data exchange over distances of more than 2.5 km. A method for distributing tasks, considering the energy supply and spatial coordinates of the UAV, is presented, which allows dynamically redistributing the load in case of failures. The proposed solutions enhance the autonomy and fault tolerance of the UAV group, minimize operator involvement, and ensure safe mission performance in a non-deterministic environment.
  The approbation of the proposed model, performed in laboratory and field conditions, showed its stability when transmitting data over extended distances and in group operation. The absence of collisions during data exchange between UAVs has been separately confirmed, which indicates the correctness of traffic separation and the effectiveness of the chosen communication architecture. The results obtained confirm the possibility of practical application of the developed models to increase the autonomy and continuity of agricultural work.
  The developed approach also demonstrates the potential for scaling, which expands the scope of its application in precision farming tasks.

• IMPROVING INTERFERENCE IMMUNITY OF GROUND-TO-AIR RADIO LINKS BY ADAPTIVE ROUTE MODIFICATION OF A UAV RELAY BASED ON INTERFERENCE ENVIRONMENT ANALYSIS

  А. А. Shmidt , V.R. Bikbulatov , D.N. Polyakov , А.А. Tkhakakhov

  2026-04-29

  Abstract ▼

  The relevance of the work is driven by the increasing intensity of electronic countermeasures in the tactical command echelon, where traditional relay communication methods with fixed routes fail to ensure the required interference immunity and signal security. The objective of this study is to develop a method for adaptive route control of a UAV relay based on continuous monitoring of the electromagnetic environment in order to improve interference immunity of ground-to-air radio links. The primary method employed is multi-criteria flight path optimization with adaptive weighting coefficients, simultaneously considering three criteria: minimization of interference levels at the relay operating frequencies, ensuring radio accessibility with network correspondents, and reduction of detection probability by enemy electronic warfare assets. To reconstruct the spatial interference pattern from a limited set of onboard measurements, several interpolation and extrapolation methods are examined: inverse distance weighting, radial basis function interpolation, and statistical extrapolation based on a spatial correlation function. A cyclic operational procedure for the adaptive routing system is developed, comprising data collection, construction of a three-dimensional interference map, prediction of its evolution, calculation of the optimal trajectory, and monitoring of the maneuver outcome. Simulation results show that the proposed method increases the signal-to-interference ratio by 1.5–2 dB on average and up to 8 dB in the worst-case scenario compared to fixed-route flight. The practical significance lies in the possibility of implementing the proposed method using existing UAV platforms and radio-electronic equipment without fundamentally new technical solutions.

• SYNERGETIC SYNTHESIS OF CONTROL LAW FOR UAV IN THE PRESENCE OF WIND DISTURBANCES WITH INPUT CONSTRAINTS

  G.E. Veselov, Ingabire Aline

  2020-07-20

  Abstract ▼

  This paper discusses the application of synergetic control theory (SCT) methods to the problem of
  control system synthesis for fixed-wing unmanned aerial vehicle (UAV) in the presence of wind disturbances.
  The main purpose of this study is to develop a synergetic method for the synthesis of nonlinear
  control systems for fixed-wing UAVs, which guarantee the asymptotic stability of the closed-loop systems
  when moving along a given trajectory, stability and adaptability with significant nonlinearity of
  mathematical models for controlling fixed-wing UAVs in the presence of wind disturbances. Furthermore,
  an important task in the synthesis of control systems for various objects, including UAV, is to take
  into account constraints on the state variables of the control object, which can be determined by both the
  energy efficiency requirements and safety systems, as well as other constraints and requirements imposed
  on these coordinates. This article proposes a procedure for the synthesis of nonlinear vector control
  systems for fixed-wing UAV by applying SCT approaches that provide invariance to external unmeasured
  disturbances, fulfillment of specified technological control objectives, asymptotic stability of
  the closed-loop system, and also take into account the introduced constraints on the UAV internal coordinates.
  The procedure suggested in this article for the synergetic synthesis of nonlinear vector control
  systems of fixed-wing UAV ensures the effective use of this type of UAV in solving various tasks, including
  the operation of such UAV as elements of a group of autonomous objects that solve a given group
  technological task. The effectiveness of the proposed approach to the synergetic synthesis of control
  strategies is confirmed by the results of computer modeling of the synthesized nonlinear vector control
  system of fixed-wing UAV. The proposed synergetic method of control system synthesis for fixed-wing
  UAV can be applied for the development of advanced flight simulation and navigation complexes that
  simulate the UAV behavior in the presence of wind disturbances and serve as a basis for improving the
  flight performance of the fixed-wing UAV.

• PROSPECTS OF MALE-CLASS UAVS USING FOR THE HUGE TERRITORIES AERIAL SURVEY

  А. М. Fedulin, D.M. Driagin

  2021-04-04

  Abstract ▼

  The aim of the study is to estimate the MALE-class (Medium Altitude Long Endurance) UAV
  (Unmanned Air Vehicles) using possibility to solve the problem of regular aerial survey of huge
  areas relative to other means used for this, such as: small-sized UAVs, satellite remote sensing
  and manned aircrafts. Considered is the issue of practical construction of onboard computer vision
  system based on a UAV “Orion” wit a ta eoff weig t of more t an a ton, w ic pro ides
  aerial photography in the visible and near infrared range and airborne laser scanning of the underlying
  surface with automatic processing of the received data on board in near real-time mode
  detecting the changes occurred since the previous survey. It has been determined the key components
  of the computer vision system both the hardware and software platform required highperformance
  computing and big-data storage. It has been presented a promising architecture,
  given estimates for its search performance, weight and power consumption, determined the typical
  flight altitude, which provides the input data spatial resolution, which is necessary for objectoriented
  change detection algorithms, based on a convolutional neural networks machine learning.
  It has been proposed organizational and technical solutions to speed up the data processing
  cycle, taking into account the requirements of the legislation regarding the declassification of
  aerial survey data. The results obtained confirm that after the issuance of the Orion UAV by the
  Federal Air Transport Agency of the aircraft type certificate, which gives the right to perform
  commercial flights in the shared airspace of the Russian Federation, it will be possible to implement
  an aerial survey complex of high productivity and degree of autonomy using cut of the edge
  CV & ML technologies. It seems the tactical, technical and economic capabilities of which proposed
  will be orders of magnitude superior to the currently existing solutions especially for hardto-
  reach regions.

• MODEL OF SCATTERING OF RADAR SIGNALS FROM UAV

  V. A. Derkachev

  2021-07-18

  Abstract ▼

  In this article, a model of scattering of radar signals from unmanned aerial vehicles (UAVs)
  of a multi-rotor type is considered for the formation of training data for a neural network classifier.
  Recently, there has been an increased interest in studying the issue of detecting and classifying
  small unmanned aerial vehicles (UAVs), which is associated with the development of the UAV
  range in sales and production. In addition to the development of UAVs, an increase in the performance
  of computers made it possible to create classifiers using new neural network algorithms.
  This model generates radar images obtained as a result of the reflection of a chirp radar signal
  from an unmanned aerial vehicle, taking into account the configuration, characteristics, current
  location and flight parameters of the observed object. When calculating the reflected signal, the
  angles of rotation of the UAV (pitch, roll and yaw), flight speed, size and location of propellers in
  the current UAV configuration are taken into account. The resulting model can be useful for the
  formation of a training set of a classifier of unmanned aerial vehicles of a multi-rotor type, builtusing convolutional neural networks. The need to use a model that generates data for a neural
  network is due to the requirement for a large number of training and verification samples, as well
  as a wide variety of configurations of unmanned aerial vehicles, which greatly increases the complexity
  and cost of creating a training dataset using experimental measurements. In addition to
  training the neural network itself, this model can be used to assess the detection and classification
  of various types of multi-rotor UAVs, in the development of a specialized radar station for detecting
  this type of objects.

• THE ESTIMATION OF CHANGING ENVIRONMENTAL CONDITIONS INFLUENCE ON THE WORKLOAD DISTRIBUTION IN THE UAV GROUP

  I.B. Safronenkova, A.B. Klimenko

  2021-12-24

  Abstract ▼

  The paper considers the problem of workload distribution in a group of unmanned aerial vehicles
  (UAVs) when monitoring a certain area in a changing environment, which has a direct impact on the
  onboard energy resources consumption. The stage of a monitoring problem-solving, which includes the
  distribution of UAVs over scanning bands, is described here. When this stage is carried, there is no
  opportunity to take into account the factors of environmental impact. But these factors are crucial
  due to the limited onboard energy resources. In this regard, a situation is very likely when the UAV is
  not able to complete the sub-task assigned to it, which jeopardizes the completion of the entire mission
  of the group. To avoid this situation, it is proposed to use the technique of a decision-making on
  the need to relocate the workload in a group of mobile robots (MR). The decision-making is based on
  the ontological analysis procedure, which allows limiting the number of choices for workload relocation.
  The ontology model of the workload distribution in a group of UAVs was developed. This model
  takes into account the possibility of additional performance involvement either by means of the resources
  of neighboring UAVs, or by means of devices of the "foggy" layer. Examples of production
  rules are given, on the basis of which a decision is made on the need to relocate the workload. A
  comparative estimation of the resources volume involved in the implementation of two methods of
  workload relocation problem solving, depending on the frequency of changes in environmental conditions,
  is carried out. The results of computational experiments have shown that the method based on
  ontological analysis is more efficient in comparison with the method based on LDG (Local Device
  Group) in terms of the amount of resources involved. This makes it possible to increase the time of joint
  mission implementation by the UAV group.

• UAV GROUP MANAGEMENT WHEN WORKING OUT OF CRISIS FLIGHT SITUATIONS IN SOLVING TRANSPORT PROBLEMS

  А.I. Savelyev, V.V. Lebedeva, I.V. Lebedev, К.V. Kamynin, L.D. Kuznetsov, А.L. Ronzhin

  2022-04-21

  Abstract ▼

  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.

• CLASSIFICATION OF RADAR IMAGES OF MULTI-ROTOR UNMANNED AERIAL VEHICLES USING THE YOLO11 ALGORITHM

  V.А. Derkachev

  171-180

  2025-07-24

  Abstract ▼

  This article discusses a classifier of radar images of unmanned aerial vehicles based on a neural network built on the YOLO algorithm version 11. Solving the problem of detecting and classifying unmanned aerial vehicles has become one of the priority tasks at present. The increase in the number of modifications of unmanned aerial vehicles greatly complicates the use of statistical classification methods, which requires the use of new approaches to solving the classification problem. The development of neural network methods, simultaneously with an increase in the performance of computers for training, on the one hand, and embedded solutions, on the other, allows for the classification of aircraft using radar images in real time. The use of the YOLO11 algorithm allows, in addition to determining the class of the target, to estimate the range to the observed object. The use of radar images is justified due to the fact that visual observation is not always possible due to difficult weather conditions and darkness. To train the neural network, it is proposed to use a set of radar images obtained using the author's model of data generation with an arbitrary configuration of unmanned aerial vehicles. The neural network of the Detection YOLO11s class (9.4 million parameters) was trained on a sample of radar images of two classes, a total of 8192. As a result of training, an accuracy of 0.99 was obtained for classification in 2 classes of objects (on test model data). Tests were conducted using natural data taken using the TI IWR1642 millimeter-range radar system, as a result of which error-free classification of objects on a small sample was achieved

• MULTIPURPOSE APPROACH TO VISUAL NAVIGATION BASED ON LANDSCAPES FOR UAVS OPERATING IN GNSS-UNAVAILABLE CONDITIONS

  S. V. Kuleshov, А. V. Kvasnov, А. А. Zaytseva, А.L. Ronzhin

  2025-04-27

  Abstract ▼

  The aim of the study is to provide the possibility of UAV navigation when it is impossible to use satellite
  global positioning systems using GNSS in electronic warfare conditions. To achieve this goal, a
  comprehensive approach to ensuring UAV navigation by visual landmarks using machine vision systems is
  proposed. It is proposed to synthesize images of the underlying surface by combining sensor data, which
  improves the quality of UAV positioning in the absence of satellite navigation systems. It is shown that
  when combining remote sensing images of different origin and changing external operating conditions
  (day-night, winter-summer, etc.), it is important to most fully localize the objects of the underlying surface.
  In the machine vision system for visual navigation of UAVs by natural landmarks, a method of electromechanical
  image scanning is proposed, which allows increasing the field of view of a camera of an arbitrary
  range. Modeling of the characteristics of the machine vision system with electromechanical scanning
  is carried out to determine the limits of applicability to the problem of visual navigation. It is shown that
  the most significant parameter of positioning accuracy is the shooting height of the underlying surface,
  which is quasi-linear under the condition of a fixed camera tilt angle, and for high-quality positioning, the
  best option is the frontal position of the camera at the nadir point. The proposed approach allows creating
  virtual 3D models of the underlying surface, thereby increasing the capabilities for more accurate recognition
  of objects based on the scale and size of the segmented areas. Measuring the camera elevation angle
  can be used to detect and recognize natural landmarks that can be predetermined (road intersections buildings or structures, utility facilities, etc.). On the other hand, the frontal position of the camera with a
  zero elevation angle is advantageous for verifying the flight route, positioning the UAV relative to the
  reference landmark. This is due to the fact that with the widespread use of software based on mathematical
  models, the photogrammetric ruler technology has become appropriate for quantitative measurement
  of terrain plans and maps

• DEVELOPMENT AND ANALYSE VISUAL NAVIGATION SYSTEM FOR AIR AND GROUND-BASED ROBOTS

  V. P. Noskov, Y. S. Barichev, О.P. Goydin, А.N. Kuryanov

  2025-04-27

  Abstract ▼

  The work is devoted to solving urgent problems of joint autonomous visual navigation for air and
  ground-based robots in urbanized environments. These environments are highly demanded for special
  operations, including dense urban areas and buildings, where the use of traditional remote control devices
  is limited due to the presence of shielded areas. The proposed solution addresses group navigation tasks
  based on data from onboard vision systems during operational reconnaissance of the working area by an
  unmanned aerial vehicle (UAV). The results of this reconnaissance enable autonomous movement and
  flight, both for individual heterogeneous robotic systems and for groups.The navigation algorithms are
  based on methods for extracting a horizontal reference surface and horizontal sections of the external
  environment from a volumetric point cloud generated by an onboard lidar. These methods allow for the
  precise and rapid determination of all six coordinates of the control object. Cases where the navigation
  task cannot be fully solved due to specific environmental characteristics are also considered. To address these challenges, methods are proposed to enhance lidar rangefinding data by integrating video camera
  data. An accuracy assessment of the video navigation solutions is provided, obtained through mathematical
  modeling of the external environment and the generation of video data. To ensure safe autonomous
  flight and movement of robotic systems in urban environments, methods for reducing video navigation
  errors are proposed. These methods utilize a specially designed bank of reference images with known
  coordinates of their formation. The effectiveness of the applied methods and the proposed video navigation
  algorithms is confirmed by experimental studies of the corresponding software and hardware in real
  urbanized environments