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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

• ALGORITHM FOR COMPLEXING MULTIPLE DATA SOURCES INTO A SINGLE OCCUPANCY MAP

  I.O. Shepel

  2021-08-11

  Abstract ▼

  The paper deals with the problem of constructing a passability model of environment with
  a large number of dynamic objects based on data from several different sensors. The aim of the
  work is to improve the algorithm for constructing the occupancy map by adding data from both
  existing algorithms for moving obstacles detection and from millimeter-wave automotive radar.
  The study solves the problem of combining data on static environment and dynamic objects into
  one general passability model for further trajectory planning. The modification of the algorithm
  presented in the article is able to combine data from both occupancy maps based on a threedimensional point cloud from any sensor such as lidar or radar, and arrays of bounding boxes of
  objects with known coordinates, sizes, and orientation. Data aggregation occurs at the level of
  building occupancy maps and does not impose requirements on the source of information about
  dynamic obstacles. The algorithm is able to refine the data on the position and size of the dynamic
  object by speed from the radar, which allows to plan the trajectory taking into account the movement
  of dynamic objects. The parallel use of the classical approach allows to detect obstacles in
  the event of an error in the output of the dynamic obstacle detection algorithm. The developed
  algorithm works in real time on the Jetson AGX Xavier module, and is tested in real conditions on
  a mobile robotic platform in autonomous mode. Promising directions for further research to improve
  the presented approach are formulated.

• PARTICLE FILTER BASED DETECTION OF DYNAMIC OBJECTS ON AN ACCUMULATED OCCUPANCY MAP

  I.О. Shepel

  2022-08-09

  Abstract ▼

  The paper considers the problem of detecting dynamic obstacles on the accumulated occupancy
  map generated by the computer vision system of a mobile robot. The purpose of this research is to
  improve the quality of the obstacle detection algorithm by adding a particle filter to find moving objects
  from the map data. In the paper, the problem of correct accumulation of data in the occupancy
  map and reducing the delay in updating the map cells in which the object moves is solved. The modification
  of the particle filter presented in the paper is able to work correctly with dynamic obstacles
  in a wide range of speeds; it is resistant to outliers caused by random generation of the initial particles
  velocities, and is workable under real conditions in real time in an environment with a lot of
  moving objects. A heuristic has been created that reduces the number of misclassifications in occluded
  areas. It is shown that the algorithm for detecting dynamic objects in the map is invariant to the
  type of sensors used in the vision system, and an implementation combined with an accumulated
  occupancy map is described. The algorithm is implemented and tested on board an autonomous mobile
  robot, as well as on an open dataset. The article also provides a comparison with other approaches
  of dynamic obstacles detection, as well as calculated performance metrics for all analyzed
  methods for computers based on the GPU Nvidia RTX 3070 and Jetson AGX Xavier. Promising directions
  for further research to improve the presented algorithm are formulated.

• 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