Browsing by Author "Khotsyanovsky, Volodymyr"

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Adaptive Control of Manipulator Robots in a Dynamic Environment Using Neural Networks
(National Aviation University, 2024-06-26) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Khotsyanovsky, Volodymyr; Хоцянівський, Володимир Петрович
The purpose of the study is to develop an approach to planning the trajectory of the manipulator robot using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of manipulator robots and the review of intelligent control systems made it possible to obtain a complete picture of the current state of this issue. A system is proposed that can perceive the environment and control the movement of the robot by generating the correct control commands. For this, 3 tasks were solved, namely: analysis of the environment in order to determine its features, determination of the trajectory in order to neutralize the collision and determination of controlled influences for the executive authorities in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks are proposed.
Camera Image Processing on ESP32 Microcontroller with Help of Convolutional Neural Network
(National Aviation University, 2022-09-26) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Khotsyanovsky, Volodymyr; Хоцянівський, Володимир Петрович
This paper analyzes a common ESP32 microcontroller with a built-in camera for image classification tasks using a convolutional neural network. ESP32 is commonly used in IoT devices to read data and control sensors, so its computing power is not significant, which has a positive effect on the cost of the device. The prevalence of ultra-low power embedded devices such as ESP32 will allow the widespread use of artificial intelligence built-in IoT devices. The duration of photographing and photo processing is obtained in the paper, as this can be a bottleneck of the microcontroller, especially together with machine learning algorithms. Deployed convolutional neural network, pre-trained on another device, MobileNet architecture on microcontroller and proved that ESP32 capacity is sufficient for simultaneous operation of both the camera and convolutional neural network.
Method of Planning and Coordination of Robot Movement Using Neural Networks for Solution of Dynamic Production Scenarios
(National Aviation University, 2024-03-29) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Khotsyanovsky, Volodymyr; Хоцянівський, Володимир Петрович
The purpose of the study is to develop an approach to planning the trajectory of a robot manipulator using an intelligent system based on neural networks. For this purpose, the work considered the processes of planning and deploying the movement of the robot. The analysis of existing methods of planning the movement of robot manipulators and the review of intelligent control systems provided a comprehensive picture of the current state of this issue. A system is proposed that can perceive the environment and controls the movement of the robot by generating correct control commands. For this purpose, 3 tasks were solved, namely, the analysis of the environment in order to determine its features, the determination of the trajectory in order to neutralize the collision, and the determination of controlled influences for the executive bodies in order to implement the movement. The functionality and structure of the neural network for solving each of the tasks is proposed. The proposed approach is compared with existing approaches on key parameters, such as the execution time of the planned movement and the time of calculating the movement trajectory. The results confirmed that the use of neural network to optimize the trajectory and dynamic prediction to avoid obstacles significantly increased the adaptability of the system to the changing conditions of the production environment, which opens up new opportunities for improving automated processes and providing optimal conditions for the functioning of manipulator robots in real-time.