COMMAND RECOGNITION SYSTEM RESEARCH
PDF
DOI
SLIB.UZ

Keywords

Mobile Robot, Control Command, Command Recognition, Robot Control System, Manufacturing Innovation, Industrial Innovation.

How to Cite

Vladyslav Basiuk, Svitlana Maksymova, & Olena Chala. (2024). COMMAND RECOGNITION SYSTEM RESEARCH. TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN, 2(5), 50–61. Retrieved from https://universalpublishings.com/index.php/tsru/article/view/5611

Abstract

This article presents experimental studies of a previously developed command recognition system. The details of the proposed mobile robot are described in detail. The setup of two experiments is presented, as well as an analysis of the results obtained. The conclusions provide recommendations for improving the accuracy of command recognition.

PDF
DOI
SLIB.UZ

References

Zacharaki, A., & et al. (2020). Safety bounds in human robot interaction: A survey. Safety science, 127, 104667.

Arents, J., & Greitans, M. (2022). Smart industrial robot control trends, challenges and opportunities within manufacturing. Applied Sciences, 12(2), 937.

Sotnik, Svitlana, et al. Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 2020, 8.5: 2074-2079.

Baker, J. H., et al. Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 2021, 69.7: 38-44.

Sotnik, Svitlana; LYASHENKO, Vyacheslav. Prospects for Introduction of Robotics in Service. Prospects, 2022, 6.5: 4-9.

Ahmad, M. A., et al. Features of the construction and control of the navigation system of a mobile robot. International Journal of Emerging Trends in Engineering Research, 2020, 8.4: 1445-1449.

Al-Sharo, Yasser Mohammad, et al. Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 2023, 30.2: 142-151.

Lyashenko, V., et al. Recognition of Voice Commands Based on Neural Network. TEM Journal: Technology, Education, Management, Informatics, 2021, 10.2: 583-591.

Lyashenko, V.; Sotnik, S. Overview of Innovative Walking Robots. International Journal of Academic Engineering Research (IJAER), 2022, 6.4: 3-7.

Maksymova, Svitlana; Matarneh, Rami; Lyashenko, Vyacheslav V. Software for Voice Control Robot: Example of Implementation. Open Access Library Journal, 2017, 4: e3848.

Lyashenko, Vyacheslav; Sotnik, Svitlana. Analysis of Basic Principles for Sensor System Design Process Mobile Robots. Journal La Multiapp, 2020, 1.4: 1-6.

Abu-Jassar, Amer Tahseen, et al. Access Control to Robotic Systems Based on Biometric: The Generalized Model and its Practical Implementation. International Journal of Intelligent Engineering & Systems, 2023, 16.5.

Al-Sharo Y., & et al. (2023). A Robo-hand prototype design gripping device within the framework of sustainable development. Indian Journal of Engineering, 2023, 20, e37ije1673.

Lyashenko, V., & et al. (2023). Automated Monitoring and Visualization System in Production. Int. Res. J. Multidiscip. Technovation, 5(6), 09-18.

Stetsenko, K., & et al. (2023). Exploring BEAM Robotics for Adaptive and Energy-Efficient Solutions. Multidisciplinary Journal of Science and Technology, 3(4), 193-199.

Yevsieiev, V., & et al. (2024). Object Recognition and Tracking Method in the Mobile Robot’s Workspace in Real Time. Technical Science Research In Uzbekistan, 2(2), 115-124.

Shcherbyna, V., & et al. (2023). Mobile Robot for Fires Detection Development. Journal of Universal Science Research, 1(11), 17-27.

Yevsieiev, V., & et al. (2024). The Canny Algorithm Implementation for Obtaining the Object Contour in a Mobile Robot’s Workspace in Real Time. Journal of Universal Science Research, 2(3), 7-19.

Maksymova, S., & et al (2024). The Bipedal Robot a Kinematic Diagram Development. Journal of Universal Science Research, 2(1), 6-17.

Yevsieiev, V., & et al. (2024). Active Contours Method Implementation for Objects Selection in the Mobile Robot’s Workspace. Journal of Universal Science Research, 2(2), 135-145.

Nevliudov, I., & et al. (2023). A Small-Sized Robot Prototype Development Using 3D Printing. In XXXI International Conference CAD In Machinery Design Implementation and Educational Issues, 12.

Yevsieiev, V., & et al. (2024). Using Contouring Algorithms to Select Objects in the Robots’ Workspace. Technical Science Research In Uzbekistan, 2(2), 32-42.

Nevliudov, I., & et al. (2023). Mobile Robot Navigation System Based on Ultrasonic Sensors. In2023 IEEE XXVIII International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED),IEEE, 1, 247-251.

Yevsieiev, V., & et al. (2022). A robotic prosthetic a control system and a structural diagram development. Collection of scientific papers «ΛΌГOΣ», Zurich, Switzerland, 113-114.

Basiuk, V., & et al. (2023). Mobile Robot Position Determining Using Odometry Method. Multidisciplinary Journal of Science and Technology, 3(3), 227-234.

Abu-Jassar, A. T., et al. Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 2021, 10.4: 1645-1654.

Al-Sharo, Y. M., et al. Neural networks as a tool for pattern recognition of fasteners. International Journal of Engineering Trends and Technology, 2021, 69.10: 151-160.

Tvoroshenko, Irina, et al. Modification of models intensive development ontologies by fuzzy logic. International Journal of Emerging Trends in Engineering Research, 2020, 8.3: 939-944.

Vasiurenko, Oleg, et al. Spatial-Temporal Analysis the Dynamics of Changes on the Foreign Exchange Market: an Empirical Estimates from Ukraine. Journal of Asian Multicultural Research for Economy and Management Study, 2020, 1.2: 1-6.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.

Nevliudov, Igor, et al. GUI Elements and Windows Form Formalization Parameters and Events Method to Automate the Process of Additive Cyber-Design CPPS Development. Advances in Dynamical Systems and Applications, 2021, 16.2: 441-455.

Mustafa, S. K., et al. HMI Development Automation with GUI Elements for Object-Oriented Programming Languages Implementation. SSRG International Journal of Engineering Trends and Technology, 2022, 70.1: 139-145.

Baranova, V., et al. Information system for decision support in the field of tourism based on the use of spatio-temporal data analysis. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9.4: 6356-6361.

Attar, Hani, et al. Zoomorphic Mobile Robot Development for Vertical Movement Based on the Geometrical Family Caterpillar. Computational intelligence and neuroscience, 2022, 2022: 3046116.

Attar, Hani, et al. Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging. Computational intelligence and neuroscience, 2022, 2022: 9140156.

Lyashenko, Vyacheslav V., et al. The Methodology of Image Processing in the Study of the Properties of Fiber as a Reinforcing Agent in Polymer Compositions. International Journal of Advanced Research in Computer Science, 2016, 7.1.

Lyashenko, V., et al. Defects of communication pipes from plastic in modern civil engineering. International Journal of Mechanical and Production Engineering Research and Development, 2018, 8.1: 253-262.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

Li, X., & et al. (2021). Complicated robot activity recognition by quality-aware deep reinforcement learning. Future Generation Computer Systems, 117, 480-485.

Xu, J., & et al (2020). Prediction-guided multi-objective reinforcement learning for continuous robot control. In International conference on machine learning, PMLR, 10607-10616.

Brunke, L., & et al (2022). Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5, 411-444.

Che, C., & et al (2024). Intelligent Robotic Control System Based on Computer Vision Technology. arXiv preprint arXiv:2404.01116.

Khan, A., & et al (2020). Graph policy gradients for large scale robot control. In Conference on robot learning, PMLR, 823-834.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.