Перегляд за Автор "Pysarenko, Andrii"

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    Deep Q-learning policy optimization method for enhancing generalization in autonomous vehicle control
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Drahan, Mykhailo; Pysarenko, Andrii
    The development of autonomous vehicle control policies based on deep reinforcement learning is a principal technical problem for cyber-physical systems, fundamentally constrained by the high dimensionality of state spaces, inherent algorithmic instability, and a pervasive risk of policy over-specialization that severely limits generalization to real-world scenarios. The object of this investigation is the iterative process of forming a robust control policy within a simulated environment, while the subject focuses on the influence of specialized reward structures and initial training conditions on policy convergence and generalization capability. The study's aim is to develop and empirically evaluate a deep Q-learning policy optimization method that utilizes dynamic initial conditions to mitigate over-specialization and achieve stable, globally optimal adaptive control. The developed method formalizes two optimization criteria. First, the adaptive reward function serves as the safety and convergence criterion, defined hierarchically with major penalties for collision, intermediate incentives for passing checkpoints and a continuous minor penalty for elapsed time to drive efficiency. Second, the mechanism of dynamic initial conditions acts as the policy generalization criterion, designed to inject necessary stochasticity into the state distribution. The agent is modeled as a vehicle equipped with an eight-sensor system providing 360 degrees coverage, making decisions from a discrete action space of seven options. Its ten-dimensional state vector integrates normalized sensor distance readings with normalized dynamic characteristics, including speed and angular error. Empirical testing confirmed the policy's vulnerability under baseline fixed-start conditions, where the agent demonstrated over-specialization and stagnated at a traveled distance of approximately 960 conventional units after 40,000 episodes. The subsequent application of the dynamic initial conditions criterion successfully addressed this failure. By forcing the agent to rely on its generalized state mapping instead of trajectory memory, this approach successfully overcame the learning plateau, enabling the agent to achieve full, collision-free track traversal between 53,000 and 54,000 episodes. Final optimization, driven by penalty, reduced the total track completion time by nearly half. This verification confirms the method's value in producing robust, stable, and efficient control policies suitable for integration into autonomous transport cyber-physical systems.
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    Hexacopter-Based Cyber-Physical System for Water Sampling with Adaptive Path Planning and Multi-Drone Coordination
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Pysarenko, Andrii; Rolik, Oleksandr
    The object of this study is a hexacopter-based cyber-physical system designed for autonomous water sampling to support environmental monitoring, addressing the problem of inefficient control under dynamic conditions. The subject focuses on integrating physical flight control and water sampling operations with cyber supervisory functions, including real-time waypoint navigation, task scheduling, and multi-drone coordination, validated as a current system component. The research investigates the system’s performance under payload variations and wind disturbances, ensuring robustness and precision in adverse environments. The purpose is to improve efficiency of water sampling through this CPS, achieving enhanced flight stability and positioning accuracy via a cascade PID control system, optimizing mission planning with adaptive cyber strategies, and increasing scalability through multi-drone operations. This approach aims to surpass traditional UAV systems by using physical-cyber integration for precise, robust, and scalable water quality assessment. The methodology combines simulation-based and analytical techniques to develop and assess the hexacopter CPS. A 6-degree-of-freedom mathematical model, based on Newton-Euler equations, was constructed in MATLAB/Simulink to simulate hexacopter dynamics, incorporating payload and wind effects. The cascade PID control system was tuned using the Ziegler-Nichols method, with iterative optimization to reduce overshoot and settling time across three scenarios: 1 kg static payload, 1.5 kg dynamic payload, and 5 m/s wind. The cyber supervisory system, implemented in ROS 2, employs graph-based algorithms (Dijkstra’s for waypoint navigation, list-scheduling for task allocation) and a consensus protocol for multi-drone coordination, tested in a 500x500 m² environment. Performance metrics, such as position root mean square error (RMSE) and attitude errors, were analyzed to evaluate system effectiveness. Results demonstrate significant improvements in water sampling capabilities. The cascade control system achieved a 40–50% reduction in position RMSE and maintained attitude errors within ±0.8° to ±1.2°, ensuring stable flight. The cyber-physical framework reduced mission time by 15% through adaptive path optimization, while multi-drone coordination increased sampling coverage by 20%, enhancing scalability. These outcomes reflect the system’s precision and robustness that highlight novel control and coordination strategies with practical value for environmental monitoring. The study provides a foundation for future ecological applications.
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    Vision-Based Neighbor Selection Method for Occlusion-Resilient Uncrewed Aerial Vehicle Swarm Coordination in Three-Dimensional Environments
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2025) Smovzhenko, Oleksii; Pysarenko, Andrii
    Uncrewed aerial vehicle (UAV) swarms provide superior scalability, reliability, and efficiency compared to individual UAVs, enabling transformative applications in search and rescue, precision agriculture, environmental monitoring, and urban surveillance. However, their dependence on Global Navigation Satellite Systems (GNSS) and wireless communication introduces vulnerabilities like signal loss, jamming, and scalability constraints, particularly in GNSS-denied environments. This study advances swarm robotics by developing a novel neighbor selection method for occlusion-resilient, vision-based coordination of UAV swarms in three-dimensional (3D) environments, addressing the problem of visual occlusions that disrupt decentralized flocking. Unlike prior research focusing on planar settings or communication-dependent systems, we model swarm coordination as an artificial potential field problem. Additionally, we evaluate performance through metrics like minimum nearest neighbor distance (collision avoidance), alignment (velocity synchronization), and union (cohesion). Using simulations in point mass and realistic quadcopter dynamics (Gazebo with PX4) environments, we assess swarm behavior across dense, default, and sparse configurations. Our findings reveal that occlusions degrade alignment (below 0.9) and distances (below 0.5 m) in dense swarms exceeding 70 agents, increasing collision risks. Our novel method, incorporating metric, topographic, and Delaunay strategies, mitigates these effects. Topographic selection achieves high alignment (above 0.9) in small swarms (up to 50 agents), while Delaunay ensures perfect cohesion (union = 1) and robust alignment across all swarm sizes. Validation in simulations confirms these results. Furthermore, our method enables communication-free coordination that matches or surpasses communication-enabled performance, with topographic selection outperforming (alignment above 0.9 vs. 0.85) in small swarms and Delaunay excelling in larger ones. This result eliminates the need for inter-agent communication, enhancing resilience and bandwidth efficiency. These findings establish a scalable, infrastructure-independent framework for UAV swarms, with practical value for autonomous operations in complex, occlusion-prone environments.