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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.

• A GENETIC ALGORITHM FOR PLANNING THE TRAJECTORY OF A GROUP OF MOBILE ROBOTS IN THE PRESENCE OF STATIONARY AND MOBILE OBSTACLES

  L. А. Rybak, D.I. Malyshev, D. А. Dyakonov, А. А. Mamchenkova

  2025-04-27

  Abstract ▼

  The article discusses a trajectory planning method for a group of mobile robots that ensures safe
  movement and eliminates the possibility of collisions both between the robots themselves and with external
  obstacles, including moving objects. The developed mathematical model considers three main collision
  scenarios: intersection of robot trajectories within the group, interaction with stationary obstacles, and the probability of collision with moving objects. Each of these scenarios is analyzed in detail to ensure
  maximum safety during movement, and their consideration allows for efficient adaptation of robot routes
  to changing environmental conditions. The trajectory of each robot is represented as a piecewise linear
  path with intermediate points, which are optimized to ensure safe movement. Special attention is paid to
  speed adaptation on different segments of the trajectory: a robot can adjust its speed based on current
  conditions to minimize the risk of collisions. To evaluate distances between objects, the Euclidean norm is
  used, allowing for the calculation of minimum distances between the centers of spherical representations
  of robots and obstacles. The problem is solved in two stages. In the first stage, a trajectory is constructed
  for the first robot, taking into account initial conditions and obstacle placement. In the second stage, trajectories
  are formed for the remaining robots, considering the already planned routes. For optimizing the
  coordinates of intermediate points and speeds, a genetic algorithm is applied, which minimizes travel time
  while ensuring safe movement. The genetic algorithm uses crossover and mutation operators to generate
  diverse solutions and performs checks to ensure compliance with safety conditions. Numerical simulations
  were conducted using Python, with the Matplotlib library used for visualization of results. During the
  experiments, 50 tests were performed with varying numbers of obstacles (from 5 to 10). Analysis of the
  results showed that as the number of obstacles increased, both the computation time and the quality of the
  generated trajectories improved. This confirms the effectiveness of the proposed method for controlling
  groups of mobile robots in dynamically changing environments