Search

Search Results

• DEVELOPMENT OF AN UNDERWATER VEHICLE ROBOTIC SIMULATOR TO STUDY METHODS OF RESIDENT AUVS AUTONOMOUS INTERVENTION WITH UNDERWATER INFRASTRUCTURE OBJECTS

  А.М. Maevsky, I.А. Pechayko, М. А. Alekseev, N. М. Burov

  2025-04-27

  Abstract ▼

  The article presents the process of developing an underwater vehicle simulator (USV) with an installed
  5-degree underwater manipulator complex (MC). The simulator is designed for complex testing of
  autonomous interaction of a marine robotic complex (MRC) with underwater infrastructure objects. In
  particular, an example of solving the problems of simulator operation with a model of an underwater panel
  of an underwater production complex (UPC) and solving the problem of determining concretions and
  their autonomous collection using the simulator and MC are considered. Modern trends in the development
  of underwater robotics are focused on the creation of resident autonomous systems capable of operating
  in remote and hard-to-reach areas of the World Ocean all year round. The development of resident
  technologies is associated with the need to reduce operating costs, minimize risks to personnel and increase
  the autonomous functioning time of underwater complexes. The use of such technologies is especially
  relevant in the conditions of offshore shelf development, where traditional methods of operating
  underwater vehicles encounter technical and economic limitations. The need to carry out work on the distant shelf is due to the increasing demand for hydrocarbon resources and the depletion of easily accessible
  deposits on the continental shelf. According to forecasts, promising deep-water areas located at
  depths greater than 1000 m have significant potential for oil and gas production. According to experts, the
  volume of recoverable reserves in such areas can amount to hundreds of billions of barrels of hydrocarbon
  raw materials, which makes the development of effective autonomous solutions a strategically important
  task for the oil and gas industry. The paper presents software and hardware solutions used in the
  implementation of the USV. A structural diagram of the design is provided; the software architecture and
  features of the use of artificial neural network (ANN) systems as part of the technical vision system (TVS)
  of the USV are described. The use of TVS allows to significantly increasing the autonomy of underwater
  manipulators when performing complex technological operations, such as capturing objects from the
  ground, working with bottom infrastructure objects, etc. In conclusion, the obtained results are demonstrated,
  confirming the operability of the adopted design, software and hardware solutions when performing
  real work in autonomous mode with mock-ups of hot-stab and torque-tool working tools and mating
  parts located on the mock-up of the UPC panel.

• AN INTELLIGENT PLANT MONITORING AND EARLY WARNING SYSTEM BASED

  А.А. Kochkarov, А. К. Kulikov, V.А. Olkhova, А. S. Stakhmich, А.N. Rybak

  2025-04-27

  Abstract ▼

  The present study is aimed at systematizing scientific knowledge about diseases of agricultural
  crops with the subsequent integration of the data obtained into automated agricultural production management
  systems. The relevance of the work is due to the need to minimize economic losses in crop production
  through early diagnosis of pathologies and optimization of phytosanitary control. As part of the study, a classification of plant diseases was carried out.The basil plant (Ocimum basilicum L.), characterized
  by high susceptibility to phytopathogens under intensive cultivation conditions, was chosen as a model
  object. To create an automated diagnostic tool, a specialized dataset was collected, including 214 images
  of basil at various stages of vegetation. The shooting was carried out under controlled conditions
  using an RGB camera. Each sample is annotated with the localization of damage and the affected area.
  Special attention is paid to the methodological aspects of the formation of data banks for biological systems.
  It has been established that the key problems are the high variability of morphological features in
  plants, the influence of environmental factors on the visual manifestations of diseases. Based on the analysis
  of the data obtained, the architecture of the early warning system is proposed, which includes three
  modules: a sensor unit – small cameras and microclimate sensors. The algorithmic block is a neural network
  model for semantic image segmentation and algorithms for assessing the dynamics of pathology
  development. The decision – making and notification interface provides recommendations for adjusting
  irrigation regimes, applying pesticides and trace elements. The convolutional neural network is trained
  based on the YOLOv11 framework using data augmentation methods (Gaussian noise, affine transformations)
  and transfer learning. Validation of the model on the test sample showed a detection accuracy of
  74.7% (F1-score = 0.72). To reduce false positives, postprocessing of predictions has been implemented,
  taking into account the spatial and temporal correlation of the data. The developed prototype demonstrates
  the potential of integrating computer vision and agronomy to create predictive control systems.
  Further research is planned to expand the dataset and increase parametrs, as well as the introduction of
  data processing algorithms on edge devices to reduce delays in decision-making. The results obtained can
  be adapted for other indoor crops, which contributes to the development of precision agriculture and reduces
  anthropogenic stress on agroecosystems

• METHODOLOGY AND PRACTICE OF INCREASING THE AUTONOMY OF GROUND-BASED ROBOTIC COMPLEXES

  S.М. Sokolov, A.A. Boguslavsky

  2025-04-27

  Abstract ▼

  Trends in the development of modern robotics and the needs of practice require an increase in the
  degree of autonomy of robotic complexes. Increasing the degree of autonomy, in turn, requires an increase
  in situational awareness and, as a result, an increase in the volume of data and the efficiency of
  their processing in real time on on-board resources. At the same time, the requirement of economic feasibility
  of the proposed solutions remains. Taking into account the fact that in domestic practice, in most
  cases, remote-controlled robots are used, it becomes necessary to increase their autonomy using existing
  technical solutions. This direction of development of robotic complexes is called the transition from remote
  control to supervisory control. Along this path, an increasing number of information management
  functions are transferred from the operator to the on-board information management system. Based on the
  analysis of the world and our own experience in the development of robotic complexes, the author's methodology
  for creating robots with an increased degree of autonomy, we have identified a key element in
  ensuring the intellectual autonomy of mobile devices. A unified software and hardware module for information
  support of mobile robots is proposed. The module is based on a vision system with an open software
  and hardware architecture. This module allows increasing the degree of autonomy of ground-based
  robotic complexes in terms of intellectual autonomy gradually, while remaining within the framework of
  economic feasibility. The open software architecture of the module takes into account the de facto variety
  of hardware solutions in existing remote-controlled mobile vehicles and allows for an increase in the degree
  of autonomy - to switch from remote control mode to supervisory control step by step, according to
  the tasks being solved and the available means. A technique for creating new or reengineering existing
  RTK samples is proposed. The methodology includes an analysis of the overall layout of the RTK with an
  emphasis on the software and algorithmic part of the on-board information and control system. This takes
  into account the conditions for matching the requirements for the sensor and computing parts. The paper
  considers examples of the application of this technique to the improvement of existing samples of groundbased
  RTCs.. Practical tasks and examples of their solution using the proposed module are presented

• COMBINING SEGMENTATION, TRACKING, AND CLASSIFICATION MODELS TO SOLVE VIDEO ANALYTICS PROBLEMS

  V.D. Matveev, А. Е. Arkhipov, I. S. Fomin

  2025-04-27

  Abstract ▼

  The task of detecting obstacles in front of a mobile robot has been successfully solved long ago using
  laser and ultrasonic sensors. However, obstacles that are not detected by these types of sensors may endanger
  the safety of the robot. To detect them in the work, it is proposed to use a technical vision system
  (STZ), the information from which is processed by a semantic segmentation neural network, which returns
  the mask of the obstacle on the frame and its class. The basis for such a network was the SAM universal segmentation
  network, which requires further development to be applied to the semantic segmentation task.
  The peculiarity of this network is its universal applicability, that is, the ability to select any objects in any
  filming situation. At the same time, SAM does not predict the semantics of the object. In this paper, an additional
  module is proposed that makes it possible to implement semantic segmentation by classifying the features
  of the selected objects. The possibility of using such a module to solve the problem of supplementing the
  network output with new information is substantiated. The classification result is then fed into the same filtering
  algorithm as the masks to ensure consistency between the result of the universal network and the complementary
  module. After integrating the module with the model, a new semantic segmentation model was
  obtained, called RTC-SAM in the work. It was used to perform semantic segmentation of a publicly available
  dataset with images of an open area. The 45% result obtained by the IoU metric exceeds the result of existing
  methods by 13%. The images of the results of using the new network shown in the work make it possible to
  verify its performance. It also describes the testing of the developed solution with a study of the performance
  of the developed model on a PC and a mobile computer. The algorithm on the mobile computer shows insufficient
  speed to enter real-time mode – more than 3.5 seconds to process one frame. In this regard, one of
  the directions of further research in the field of improving system performance.

• ONBOARD ACTIVE-PULSE UNDERWATER VISION SYSTEM THROUGH THE AIR-WATER BOUNDARY

  Y. К. Gruzevich, Y.N. Gordienko, P. S. Alkov, D.V. Volkov, М.S. Khodakovskaya

  2025-04-27

  Abstract ▼

  The objective of this work is to create a system for detecting underwater objects intended for installation
  on surface platforms (aircraft or remotely piloted aircraft). Systems of this type can be used to solve
  a wide range of problems in various areas of the national economy: searching for rare fish and marine
  mammals, determining their migration routes, diagnostics and laying underwater pipelines and fiber optic
  cable networks, monitoring seawater pollution, searching for sunken ships and archaeological treasures,
  and carrying out rescue operations. To solve this problem, the process of laser radiation propagation to
  an object across the air-water interface was described, a rough sea surface was modeled, and a number of
  mathematical assumptions and approximations were proposed. In the practical part, a structural diagram
  of a laser optical-television active-pulse underwater vision system was developed, including receiving and
  transmitting channels, as well as a control device consisting of an image processing unit and a control controller. The receiving channel includes an electron-optical converter of the III+ generation, highly
  sensitive in the spectral range of sea water transparency. The main element of the transmitting channel is
  a highly efficient pulse laser emitting in the spectral range of sea water transparency. The assembled device
  has undergone field tests, as a result of which it became clear that detection and recognition of underwater
  targets from an aircraft through the air-water interface using the generated image is possible,
  the maximum detection range and recognition of underwater targets of the active-pulse underwater vision
  system from an aircraft through the air-water interface is mainly determined by: attenuation of optical
  radiation in sea water and the power of the illuminating laser pulse radiation, At the same time, a distinctive
  feature of the active-pulse underwater vision system is that the increase in the detection and recognition
  range is almost directly proportional to a certain level of laser radiation power, and a further increase
  in power leads to an insignificant increase in range

• SUBTRACTION OF BACKPROPAGATION INTERFERENCE BASED ON POLARIZATION IN UNDERWATER VISION SYSTEMS FOR OPERATION IN TURBID WATER

  N.А. Budko, А.Y. Budko, М.Y. Medvedev

  2022-08-09

  Abstract ▼

  The study of the sea depths in order to ensure safety, the effective use of underwater resources
  is an urgent task. The first part of the article briefly considers the physical phenomena and
  limitations that arise during the propagation of electromagnetic waves in the visible range in the
  underwater environment. It is shown that underwater vision systems (as a class of specialized
  technical vision systems - TVS) based on conventional CCD matrices face a number of fundamental
  limitations in terms of improving the efficiency of functioning in natural water of low transparency.
  In particular, the use of artificial light sources as part of underwater vision systems in turbid
  water leads to the occurrence of backpropagation interference (BPR), which leads to spurious
  illumination of the optical device matrix. As a promising direction in the development of underwater
  vision systems, it is proposed to use methods for subtracting POR based on information about
  the polarization of light. In the review part of the article, the latest achievements in this field are
  considered. The main part of the article presents the methodology for studying the proposed method
  for subtracting the POR based on a comparison of the results obtained by processing images
  with known methods for estimating the Stokes vector parameters DoLP and AoLP, which allow
  obtaining information about the degree of polarization and the prevailing polarization angles of
  the scene, respectively. The experimentally obtained results of processing an underwater scene in
  water of varying degrees of turbidity using the DoLP, AoLP algorithms and the proposed methods
  for subtracting the POR are presented. Distinctive features are the use of four rather than two
  polarization directions in calculations, as well as the original mathematical apparatus for processing
  signals from the machine vision camera matrix.

• AN INTELLIGENT SYSTEM OF TECHNICAL VISION FOR DETECTING OBSTACLES AND PREDICTING THE BEHAVIOR OF MOVING OBJECTS ON RAILWAY TRACKS

  D.L. Shishkov, М.N. Zaripov, R.А. Gorbachev

  2022-04-21

  Abstract ▼

  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.

• CONTROL SYSTEM DESIGN AND AUTONOMY FOR TWO-WHEELED MOBILE ROBOT

  А. А. Tkachenko, D.D. Devyatkin

  2022-04-21

  Abstract ▼

  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.

• CONTACT AND VISUAL BASED ENVIRONMENT CLASSIFICATION FOR MOBILE ROBOTS

  V.P. Noskov, I.V. Rubtsov, K.Y. Mashkov, A.V. Vazaev

  2021-04-04

  Abstract ▼

  To increase the capabilities and expand possible applications of robotic systems for special
  purposes, switching from the currently being adopted remote control systems to semi-autonomous
  systems that monitor operator actions and perform part of his functions is proposed. Moving to
  autonomous control systems capable of functioning in the "silent" mode, in shielded areas andbeyond the range of radio communications is proposed as a next step. Such intellectualization of
  on-board control systems will allow to eliminate fundamental limitations and disadvantages
  caused by the communication channel and ensures the implementation of robot group control. It is
  shown that the basis for robot autonomy increasing of on-board control systems, both for movement
  and tool control, is onboard environment model generation and determining the coordinates
  of the control object. External environment model and current coordinates makes it possible to
  automate the trajectory planning and movement, which ensures the autonomous functioning of
  robotic systems. The complex problem of environment segmentation according to geometric and
  ground passability is considered, taking into account the characteristics of the chassis, the geometry
  of the relief and the supporting properties of the ground. The existing methods are described
  and the results of experimental studies are given to solve the following main tasks: – classification
  of the operation zone according to geometric passability based data from the onboard technical
  vision system; – ground types recognition according to the integrated technical vision system; –
  using the apparatus of neural networks to improve the reliability of ground types recognition; –
  determination of ground reference characteristics by measuring the reactions of the chassis during
  movement. The promising directions for further research in integrating tactile and visual information
  to improve the reliability of the classification of operation zone according to the complex
  criterion of geometric and support passability are formulated.

• AUTOMATED LANDING OF AN UNMANNED HELICOPTER TO AN UNEQUPPED SITE

  N.V. Kim, V.P. Noskov, I.V. Rubtsov, V.A. Anikin

  2020-07-10

  Abstract ▼

  Unmanned helicopters perform many tasks in difficult operating conditions and are subject to various destabilizing factors that significantly affect flight safety. The main problems encoun-tered in the operation of unmanned helicopters are considered. It is shown that the insufficient level of flight safety is caused, in particular, by the high frequency of crashes during forced land-ings. The necessity of creating onboard means of automatic landing of an unmanned helicopter is proved. Taking into account the requirements of the Federal aviation regulations for landing plac-es, the parameters-restrictions that allow formalizing the choice of terrain areas suitable for land-ing according to the onboard technical vision system are formulated. On the basis of comparative analysis, it is shown that at present, when forming the initial video data for solving this problem, it is advisable to use a complex system of technical vision based on mutually adjusted and having a common viewing area of a 3D laser sensor, color video camera and thermal imager. The proposedrecognition algorithms of the pick-up location in the video data on-Board complex system of tech-nical vision with the use of geometric criteria and the reference permeability. It is proposed to perform the recognition of landing places based on the criterion of geometric cross-country capa-bility in two stages: at the first stage, a map of terrain heights is formed based on 3D laser sensor data, and at the second stage, areas suitable for helicopter landing are selected. Recognition of suitable and unsuitable areas is performed by comparing the elevation differences of this terrain with the reference elevation differences defined for this unmanned helicopter. It is proposed to perform the recognition of suitable landing sites based on the criterion of reference passability by calculating the Euclidean distance between the obtained data and pre-known standards corre-sponding to different types of soil in the six-dimensional feature space (height variance, reflected signal intensity, three colors, and temperature). The final selection of suitable places for planting is proposed to be made from sites that meet both criteria. The results of the work of the corre-sponding software and hardware in real conditions are presented, confirming the correctness and effectiveness of the proposed algorithms.

• SOFTWARE AND HARDWARE FOR MOBILE MEANS INFORMATION SUPPORT ONBOARD SYSTEMS WITH USE OF VISION SYSTEM

  S.M. Sokolov, А. А. Boguslavsky, S.A. Romanenko

  2020-07-10

  Abstract ▼

  Research objective is increase of autonomous mobile means information support efficiency with use of visual data and technologies of its development due to rational use of nonconventional calculators and special preparation of algorithmic providing. Use of nonconventional, heteroge-neous computing means allows to expand significantly a circle of visual data processing problems on the real time scale and, thereby, to increase situational awareness of autonomous robots and efficiency of its receiving. Rational use of nonconventional calculators demands essential altera-tion of algorithmic providing. The majority of visual data algorithms were developed counting on realization on traditional, von Neumann architecture of calculators and demand essential efforts for realization on parallel structures and developments of special programming tools. In work the emphasis on researches in the area use FPGA is placed and a number of approaches in special preparation of necessary algorithmic is considered. As model, demonstration examples of use of nonconventional calculators realization of such algorithms of visual data processing which areactively used in a wide range of information support problems of purposeful movements of mobile robot is considered. Preparation and realization on FPGA of such algorithms as creation of histo-grams, an optical flow calculation, segmentation of images is described. Results of experiments are given in the operating models of airborne computers. As basic data the visual data collected at the movement of mobile means in the conditions of habitat are used. All used software is executed on the basis of the unified software framework of the real time vision systems of domestic devel-opment. In the conclusion further steps in the specified direction, taking into account aspiration to use of domestic software and hardware are discussed.

• DISTRIBUTED SYSTEM FOR BARCODE RECOGNITION USING NEURAL NETWORKS

  А.Y. Yurchenko , М.Y. Polenov

  70-79

  2025-10-01

  Abstract ▼

  This work presents a distributed software-hardware system for automated barcode recognition on moving objects in industrial environments. The primary objective of the research is to develop a reliable and adaptive solution capable of consistently reading barcodes regardless of the orientation, speed, or height of objects moving along a conveyor belt. The main focus is not on achieving maximum processing speed, but rather on providing a wide field of view and ensuring reliable recognition of moving objects. Unlike traditional scanners that require precise positioning and expensive hardware, the proposed approach leverages a single network camera and a server equipped with neural processing modules, providing a cost-effective and versatile alternative suitable for a wide range of industrial applications. A key component of the system architecture is a neural image restoration module based on the MPRNet model, which effectively reduces motion blur and optical distortions in video frames. After preprocessing, frames are passed to an object detection module built upon the YOLO architecture, which has been adapted specifically for barcode recognition. Detected barcode data is stored in a database using an ORM interface, enabling seamless integration with existing enterprise systems. To prevent frame loss and maintain high throughput, the system incorporates asynchronous processing mechanisms using multithreading and buffered queues. The relevance of this research stems from the widespread use of barcodes as the primary method of product marking in industrial settings and the increasing demand for automation in product tracking and inventory control. Despite the availability of various vision-based and scanning solutions, most existing systems are not designed to handle unstable or low-quality video streams. The proposed system demonstrates robustness to visual distortions and motion-related artifacts, making it suitable for deployment in real production environments. Its affordability and adaptability also open up possibilities for implementation in logistics, warehousing, and supply chain management.

• EMULATION OF A TECHNICAL VISION SYSTEM BASED ON VIRTUAL IR AND ULTRASONIC SENSORS FOR MOBILE ROBOT NAVIGATION

  F.М. Tseeva , N.Е. Arabov , А.М. Bozieva , Z. V. Shomakhov

  2026-04-29

  Abstract ▼

  The relevance of this research is driven by the growing need for safe validation of navigation algorithms for autonomous mobile robots operating in cluttered and dynamically changing environments, where the use of physical equipment entails risks of damage and high costs. The aim of the work is to develop a rigorous methodology for software emulation of a technical vision system based on complementary virtual infrared and ultrasonic sensors. To achieve this aim, the following tasks were solved: formalization of the kinematic model of a differential drive with a state vector [x, y, θ]ᵀ; mathematical description of nonlinear triangulation for IR sensors and the physics of ultrasound propagation using the time-of-flight method d = c·t/2; integration of additive Gaussian noises with parameters σ_us = 0.005 m, σ_ir = 0.002 m; implementation of heterogeneous data fusion using an Unscented Kalman Filter. The navigation controllers employed were the artificial potential field method with attractive and repulsive components, and fuzzy logic controllers. Experimental validation in a Python simulation environment of a maze with static obstacles demonstrated an average positioning error of 0.2 m in spherical configurations and an obstacle detection accuracy of 89.61%. The novelty of the proposed approach lies in providing a deterministic link between theoretical trajectory planning and physical implementation through the synergistic use of optical and acoustic sensory modalities. The practical significance of the work consists in a substantial reduction in the development and testing time for intelligent robotic systems, owing to the possibility of preliminary debugging of perception, data fusion, and navigation algorithms in controlled emulation conditions without the need for expensive hardware

• GROUP VIDEO NAVIGATION OF HETEROGENEOUS ROBOTS

  V.P. Noskov , О.P. Goydin , А.N. Kuryanov

  2026-04-29

  Abstract ▼

  This paper addresses the pressing challenges of collaborative autonomous video navigation of unmanned aerial vehicles and ground robots in urban environments, including dense urban development and buildings, as well as in rugged terrain, including mountainous and wooded areas, where, as in urban environments, the use of traditional remote control and navigation tools may be limited by the presence of shielded areas. It is proposed to solve group navigation problems using data from an onboard vision system during operational reconnaissance of the work area by an unmanned aerial vehicle. The results ensure autonomous movement and flight of both individual heterogeneous robotic systems and in a group. The navigation algorithms are based on the methods and algorithms for processing data from the onboard vision system, consisting of a complex of mutually adjusted lidar, television camera and thermal imager, which form the geometry of the surrounding space in the form of a point cloud with the distribution of color and temperature fields on it, allowing for the effective solution of the SLAM problem (determination of the current coordinates of the control object with the formation of a geometric model of the external environment) and the classification of the working area according to the criteria of geometric and support cross-country ability, which ensures autonomous flight and movement of robots for air and ground use in urbanized environments and on rough terrain. It is proposed to use an information and navigation field, represented as a visibility graph, to organize the autonomous operation of aerial and ground robots, including in a group, and a set of reference images, allowing for the correct execution of planned trajectories, taking into account errors in the video navigation task. This information and navigation field allows for the compact presentation of information necessary and sufficient for the autonomous operation of unmanned aerial vehicles and ground robots and facilitates its exchange between group members. The results of experimental studies in real-world conditions of urbanized environments and rugged terrain are presented, confirming the effectiveness of the proposed methods, algorithms, and corresponding software and hardware

• METHOD FOR INFORMATION INTERACTION BETWEEN AN OPERATOR AND AN AUV EQUIPPED WITH A HYDROACOUSTIC COMMUNICATION CHANNEL FOR TARGET OBJECT IDENTIFICATION

  А.Y. Konoplin , А.P. Yurmanov , А. Y. Rodionov , R.P. Vasilenko , М.О. Panchuk

  2026-04-29

  Abstract ▼

  The execution of inspection and manipulation operations by autonomous unmanned underwater vehicles (AUVs) under operator supervision is limited by the low bandwidth of the hydroacoustic communication channel, which does not allow the transmission of video streams and high-quality photographic images in real time. At the same time, existing methods for automatic recognition of target objects by onboard machine vision systems of AUVs do not guarantee accurate determination of the object’s position and shape when defects are present on its surface (siltation, biofouling, mechanical damage). To address this problem, the paper formulates the task of developing a method that enables information interaction between the operator and the AUV via a low-bandwidth hydroacoustic communication channel. This interaction allows the operator to evaluate the quality of target object recognition and subsequently generate target designations for the vehicle and its onboard manipulator. The proposed method is based on an algorithm for identifying objects with defects, which employs a modified ICP point cloud registration approach with the exclusion of defective regions and characteristic features (CFs). The detected CFs and defect regions are transformed into a compact set of geometric primitives transmitted to the operator through the hydroacoustic communication channel with the AUV. Based on the received information, incoming telemetry from the AUV, and a reference three-dimensional model of the object, the graphical interface visualizes the scanned scene, allowing the operator to assess the recognition accuracy. Full-scale experimental tests of the method were conducted using a hydroacoustic modem manufactured by the Institute of Marine Technology Problems, Far Eastern Branch of the Russian Academy of Sciences (IMTP FEB RAS) and an onboard computer NVIDIA Jetson TX2. The information packet transmitted within
  1 minute contained the object type, its position, a defect map, and the coordinates of the characteristic features, with a data volume three orders of magnitude smaller than that required for transmitting the full point cloud of the scanned object. Based on the received information, the operator confirmed successful object identification even when up to 30% of the surface area was covered by defects. The practical significance of the proposed method lies in enabling the execution of critical missions under operator supervision in conditions of environmental uncertainty

• METHOD OF SPACE CONTROL BY A DISTRIBUTED VISION SYSTEM

  S.М. Sokolov , А.А. Boguslavsky , А. B. Bugerya , М.А. Ilienkov

  2026-04-29

  Abstract ▼

  With the growing demand for mobile robotic complexes with an increased degree of autonomy, the demand for their information awareness increases significantly. Vision systems play a leading role in ensuring this awareness. To support the functioning of groups, as well as each individual robot, it is necessary to form an information field that allows for prompt decision-making in the control systems of both individual robots and the entire group as a whole. The desire to assign an increasing number of independently solved tasks to the robotic complexes requires an increasingly broad and detailed consideration of the field of operation from the VS. Distributed space surveillance and control systems are one of the promising areas for solving this problem, ensuring the formation of a unified information field based on cooperative perception, joint localization and semantic interpretation of the situation. The paper considers the problem of monitoring a given area of space by a distributed vision system in order to detect objects of interest with a known characteristic size in this area. At the same time, it is assumed that video cameras can be located on both ground and air vehicles. The layout of a distributed vision system for solving the formulated problem is described. The core of the described system is a unified information module with an omnidirectional recording unit.
  The module acts as an agent of the distributed VS. Several such modules are distributed over the area of space indicated on the map, taking into account the terrain features and other objects located in this area. The problem of choosing the optimal composition of a distributed system according to a number of criteria is solved. A methodology is proposed for planning the location of agents with specified characteristics of recording units on a given 3D terrain map to ensure the detection of objects of interest with known characteristic dimensions. An algorithm is given for calculating the locations of VS with fixed fields of view for monitoring a given area of rough terrain in order to identify static objects with known characteristic dimensions and differences from the surrounding background in reflectivity or moving objects