Факультет інформатики

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Comparative Analysis of Development Environments for UAV Software Development
(2025) Budilova, Sofiia; Kurochkin, Andrew
Unmanned aerial vehicles are in demand nowadays due to their ability to perform various tasks, both military and civilian, without the involvement of humans. The UAV simulators (UAV algorithm development environments) have a great value at the present day since they present a way to test all the new UAV algorithms that are spreading more and more finding real-world applications across various disciplines. A plethora of simulators already exist. All of them have their own advantages and disadvantages. This presents difficulties for developers to opt for the most suitable one to meet their requirements. This paper reviews the most popular UAV simulators. It also provides statistics regarding the amount of papers in general and throughout the years related to simulators and also to flight control software (e.g., ArduPilot). This provides the opportunity to observe tendencies in UAV simulation technologies. Subsequently, research was conducted to analyze scenes (usually called worlds) of the most popular and robust simulator, namely Gazebo, that are available open-source online. тA list of them is provided in this thesis. Eventually, a research gap was found, namely a shortage of Gazebo worlds containing moving objects. A new world with a moving car was created and might be used, for example, for the testing of the UAV object-tracking algorithms.
Development and Implementation of a Military Technology Trends Monitoring System
(2025) Prokhorov, Oleksandr; Kurochkin, Andrew
This work presents the design and implementation of a system for monitoring technological trends in the military sector using Telegram as a data source. The system automatically collects, processes, and analyzes both historical and real-time posts from selected Telegram channels, focusing on the emergence and dissemination of key terminology such as "реб" (eng.: "electronic warfare") in our evaluation case study. A modular architecture was developed, combining Go-based data scraping, Python-based aggregation and keyword analysis, and a Grafana dashboard for visualization. The system supports both local Docker-based deployment and cloud-based deployment via Terraform on AWS. Evaluation included performance benchmarks, peak resident-set size (RSS) profiling, and a case study comparing our system’s findings against professional media and Google Trends. Results indicate that a Telegram-based pipeline can detect rising interest in electronic-warfare topics earlier than traditional information channels.
Development of a Control Algorithm for a Two-Axis Gimbal for Object Tracking on UAV
(2026) Kulyk, Zorian ; Kurochkin, Andrew
This work presents the design, implementation, and evaluation of a control algorithm for active object tracking on a low-cost, two-axis (pitch-roll) brushless gimbal intended for UAV deployment. The platform combines a Raspberry Pi 4 for vision processing, a SpeedyBee F405 flight controller communicating over MAVLink, an IMX378 camera, and a gimbal based on an 8-bit controller board. The vision pipeline is built entirely on OpenCV and compares two correlation-filter trackers, MOSSE and CSRT. Pixel error from the tracker is mapped directly to gimbalPWMcommands through a proposed conversion with a spatial resolution coefficient derived from the camera intrinsics. The work also derives the forward kinematics of the two-axis pitch–roll configuration and establishes its theoretical limitations. Experimental results obtained on the assembled static platform provide an evaluation of the object tracking error and confirm that the proposed approach is feasible within the stated hardware constraints.
Optimization Methods for Hyperparameter Selection in UAV Object Detection Systems
(2026) Cherkasova, Mariia ; Kurochkin, Andrew
This thesis investigates hyperparameter optimization methods for object detection models deployed on a UAV platform. Detection in aerial imagery is challenged by varying object scales, complex backgrounds, sensor-induced image degradation, and motion complexity — the simultaneous movement of targets, the camera gimbal, and the UAV platform itself. These factors make model performance highly sensitive to hyperparameter configuration. The work was conducted on an existing UAV object detection codebase using a multisource dataset comprising aerial and ground-level imagery of military and civilian objects. The contributions include implementing a two-stage hyperparameter optimization pipeline using Optuna — combining random search with a subsequent TPE-based optimization — as well as redesigning the data preprocessing pipeline. The HPO-tuned model achieved mAP@0.5 = 0.623 compared to 0.564 for the baseline (+10.46%). The F1 score improved from 0.51 to 0.58 (+13.73%). Statistical significance was confirmed using a Wilcoxon signed-rank test (p = 0.004, α = 0.05, Cohen’s d = 0.693).
Planning and Control of Autonomous UAV Missions
(2026) Prokhorov, Bohdan ; Kurochkin, Andrew
As robotics systems become more advanced, enabling them to complete missions without human control becomes more important. In this work, we researched how to design and develop a real-time visual servoing solution for a quadrotor UAV equipped with a companion computer, enabling autonomous flight and target tracking. We present an Image-Based Visual Servoing (IBVS) controller that guides the UAV to approach and hover over a target using solely a lightweight object tracking neural network within a closed-loop control framework. Implementing this system solution requires no prior knowledge of the target and operates without GPS during the control phase, relying entirely on visual feedback to accomplish the task defined by the operator.
Training YOLO Models for Real-Time Object Detection on UAV
(2025) Solovei, Tymofii; Kurochkin, Andrew
In this study, we have explored the implementation of the YOLOv8(nano) model for the task of real-time detection of military objects for UAV companion computers. We have collected and merged different datasets from the open sources with clearly annotated classes such as tanks, armored vehicles, armored personnel carriers, etc. Additionally, datasets with civilian people and vehicles have been included to address ethical concerns. Because of the poor quality of original datasets, we developed a processing pipeline for proper data selecting, filtering, and augmentation. We trained the YOLOv8-nano model for 100 epochs. The default pre-trained on the COCO dataset YOLOv8-nano model initially achieved an mAP@0.5 of 0.305 and mAP@0.5-0.95 of 0.169 on our dataset. Our final YOLOv8-nano model achieves a mAP@0.5 of 73.61% and mAP@0.5-0.95 of 51.18%. We also evaluated our model using combat videos from FPV(First Person View) drones containing different military targets.

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