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• 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

• THE UNMANNED AMPHIBIAN AIRCRAFT’S TECHNOLOGIES OF COMPLEX NAVIGATION IN THE AVIATION WATER AREA

  E.V. Voloshchenko, V.Y. Voloshchenko

  2021-02-13

  Abstract ▼

  The paper considers the development of technologies for integrated high-precision navigation
  of unmanned amphibian aircraft (UAA) to ensure both positioning and navigation on the surface in
  conditions of limited atmospheric visibility (low cloudiness, masking effect of hydrometeors, night
  time, etc.) in the seadrome’s water area using hydroacoustic a remote control channel operating
  through the use of a bottom network structure of original transmitter-receiver antenna assemblies
  (TAA). Each individual TAA is proposed to be used as an "omnidirectional" sonar bottom beacon in
  the upper hemisphere, consisting of electroacoustic transducers (ET), each of which operates in the
  mode of the parametric transmitting array. The statically generated "partial" lobes of the resulting
  DP of single TAA are uniformly quantized over the bodily sectors in the hemisphere; moreover, due
  to the use of nonlinear acoustics effects, an individual "frequency coloration" of each of the bodily
  sectors is possible. As a result, an individual distribution of “frequency-colored spots” of local ultrasonic
  irradiation can be formed at the “water - air” interface of a given section of the aviation water
  area, and, both continuous and discrete, the latter can be considered as separate points of the required
  trajectory of the UAA, radio electronic equipment which tracks the "acoustically marked"
  section of the required direction of the wiring located ahead of the course.

• ORGANIZATION OF GOAL-DIRECTED MOVEMENTS OF VEHICLES USING VISUAL LANDMARKS

  S.M. Sokolov, N.D. Beklemishev, А. А. Boguslavsky

  2021-04-04

  Abstract ▼

  The report considers the solution of the navigation problem with the help of a technical vision
  system that determines the position of the mobile vehicle relative to the landmarks indicated
  in the surrounding space. Navigation by landmarks is the most objective criterion for the location
  of a mobile vehicle in the surrounding space. The method of measuring the parameters of the ratios
  that characterize the location of the mobile vehicle relative to the landmarks is almost independent
  of other navigation measurements. Data input for correcting coordinates and other motion
  parameters can be performed not continuously, but at some discrete, and, in general, quite
  rare moments of time. The general scheme of the solution is considered: from setting up, to receiving
  navigation information. The integration of the obtained data with data from other navigation
  tools is briefly described, and the key problems and parameters of the VS that affect the accuracy
  of the obtained results are analyzed. The key point in this method is the solution of a system of
  equations describing the position of robotic complexes relative to the specified landmarks. This
  system is solved by a modified Gauss-Newton method for a nonlinear redefined system of equations.
  By replacing the left side of each equation with its differential at the point of initial approximation,
  linearization is performed. The values of the unknowns in the redefined system of linear
  equations for which the sum of the squared residuals in the equations is minimal can be obtained
  either by the SVD (singular value decomposition) method or by using the system's symmetrization.
  At the same time, SVD is more resistant to the accumulation of computational error, but it is
  somewhat more demanding on computer resources and more difficult to implement. We used the
  symmetrization solution as a simpler one. The resulting system is solved by the square root
  (Cholesky) method. To detect landmarks in the VS, two types of VS modules are used – panoramic,
  based on a camera with a fish-eye lens, and stereo. The proposed method allows us to solve the
  problem of clarifying the parameters of motion by separate, sparse measurements of the proper
  position and speed relative to landmarks in the surrounding space. Independently and in combination
  with other navigation tools, the described approach provides high-precision determination of
  navigation parameters in various driving conditions. The results of field experiments with the
  model of the proposed system in motion under various conditions are described. The ways of improvement
  and development of the considered approach are discussed.

• REALTIME NEURAL NETWORK ALGORITHM FOR FULL-FRAME MARINE SURFACE OBJECTS RECOGNITION

  V.A. Tupikov, V.A. Pavlova, V.A. Bondarenko, N.G. Holod

  2020-07-10

  Abstract ▼

  The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.