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• 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.

• 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.

• 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

• 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.