Search

Search Results

• ORGANIZATION OF MOBILE ROBOTS NAVIGATION BASED ON COGNITIVE MAPPING

  А.М. Korsakov , V. V. Ivanova

  2026-04-29

  Abstract ▼

  The article addresses the relevant task of ensuring the autonomy of mobile robots in complex conditions, where the use of traditional navigation methods based on global coordinate systems and satellite data is impossible or ineffective. To solve this problem, an approach based on cognitive (interpretive) navigation is proposed, where semantic understanding of the environment plays the central role. The key feature of the method is the construction of a cognitive map – a semantically oriented graph whose vertices correspond to landmark objects (or groups of homogeneous landmarks), and whose edges correspond to fixed sets of information-motor actions (elementary conditioned behavioral patterns). Thus, the robot's route while moving along the cognitive map is reduced to a fixed set of information-motor actions. The map construction process is carried out automatically based on a pre-obtained semantically segmented image of the terrain, which allows the mobile robot to acquire a priori information about the relative positions and shapes of the landmarks. To formalize the navigation process and manage the robot's behavior based on the cognitive map, the authors propose a specially developed formal language, LRNB (Language of Robot Navigation Behavior). This language allows the decomposition of complex missions into elementary information-motor actions, the specification of their completion conditions, and the description of interaction scenarios with dynamic and static objects. The work details the principles of building a cognitive map, the syntax of the LRNB language, and the mechanism for forming a route as a sequence of commands. The practical part includes the results of verifying the approach in a simulation environment using a specially developed emulator, as well as preliminary field tests on a laboratory tracked mobile robot, which confirmed the fundamental feasibility of the proposed approach. The obtained results indicate the potential of the method for application in critically important scenarios, such as disaster zones, areas of electronic warfare, and other environments with a high degree of uncertainty. Further work plans are proposed, related to bringing experimental conditions closer to the real-world conditions of potential operation.

• APPLICATION OF COMPUTER VISION TECHNOLOGIES IN VISUAL INFORMATION PROCESSING SYSTEMS

  О.B. Lebedev , R.I. Cherkasov

  254-276

  2025-11-10

  Abstract ▼

  This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are studied in detail with an emphasis on the mechanisms of spatial feature extraction through convolutional layers, optimization of data representation through pooling operations and feature transformation in fully connected layers. Particular attention is paid to the evolution of object detection methods, where the problem of model selection is considered as an extension of classification due to the integration of spatial coordinate regression, and an assessment of the effectiveness of detectors is carried out based on the IoU, Precision, Recall and F1-score metrics, demonstrating a fundamental trade-off between localization accuracy and processing speed. The YOLOv7 algorithm is presented as an optimal solution for real-time systems. Its architecture is based on splitting the input image into a grid of S×S cells with direct prediction of the bounding box parameters (center coordinates, width, height) and class probabilities for each cell, as well as the use of specialized layers (SPP, PANet) for multi-scale feature aggregation. The structure of the neural network confirms the effectiveness of the approach used, which ensures high performance without critically reducing accuracy in strategically important applications of video surveillance, autonomous systems, and augmented reality. A comparative study of one-stage and two-stage detectors was conducted with an assessment of their performance by key metrics. Particular attention is paid to the practical aspects of using computer vision technologies in real visual information processing systems.

• INTELLIGENT CONTROL OF ROBOTICS AT RODENT BURROW SEGMENTATION USING DEEP CONVOLUTIONAL ARCHITECTURES

  М.А. Astapova

  19-30

  2025-04-27

  Abstract ▼

  In this paper, we investigate the application of neural network architectures for semantic segmentation
  of rodent burrows for monitoring their population in agricultural fields. In particular, three models
  for semantic segmentation are considered: convolutional autoencoder (CAE), SegNet, and U-Net. These
  models are applied to analyze images obtained from unmanned aerial vehicles (UAVs) and ground robotic
  means, which allows for automatic burrow detection, minimizing the need for labor costs in processing
  large amounts of data. A sample of 247 RGB images containing 1098 labeled burrows was prepared for
  training and testing the models. The quality indicators of semantic segmentation were assessed using the
  Jaccard metric (IoU), which resulted in the following values: 0.511 for CAE, 0.548 for SegNet, and 0.529
  for U-Net. An assessment of the computational resources required to implement these models in on-board
  computing units (OCUs) of mobile robotic means was conducted. Two criteria were considered: the number
  of floating-point operations (GFLOPS) and the number of model parameters. The results showed that
  SegNet requires 2.23 GFLOPS and has 0.76 million parameters, which is 2.58 and 2.33 times less than
  SAE and U-Net, respectively. The number of floating-point operations for SegNet was also 2.43 and 1.88
  times lower than that of SAE and U-Net, respectively. As a result, SegNet outperformed SAE and U-Net in
  both segmentation efficiency and required computational resources. This work was carried out as part of
  the implementation of a computer vision system for an agricultural robotic means.