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
