Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment
), Abstract
The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications.
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References
M. A. Ahmad, I. Tvoroshenko, J. H. Baker, and V. Lyashenko, Modeling the structure of intellectual means of decision-making using a system-oriented NFO approach, Int. J. Emerg. Trends Eng. Res., vol. 7, no. 11, pp. 460–465, 2019.
H. Attar, A. T. Abu-Jassar, V. Yevsieiev, V. Lyashenko, I. Nevliudov, and A. K. Luhach, Zoomorphic mobile robot development for vertical movement based on the geometrical family caterpillar, Comput. Intell. Neurosci., vol. 2022, p. 3046116, 2022.
H. Attar, A. T. Abu-Jassar, A. Amer, V. Lyashenko, V. Yevsieiev, and M. R. Khosravi, Control system development and implementation of a CNC laser engraver for environmental use with remote imaging, Comput. Intell. Neurosci., vol. 2022, p. 9140156, 2022.
S. M. H. Mousavi, V. Lyashenko, and V. B. S. Prasath, Analysis of a robust edge detection system in different color spaces using color and depth images, Comput. Opt., vol. 43, no. 4, pp. 632–646, 2019.
A. T. Abu-Jassar, Y. M. Al-Sharo, V. Lyashenko, and S. Sotnik, Some features of classifiers implementation for object recognition in specialized computer systems, TEM J., vol. 10, no. 4, pp. 1645–1654, 2021.
A. C. Bavelos, N. Kousi, C. Gkournelos, K. Lotsaris, S. Aivaliotis, G. Michalos, and S. Makris, Enabling flexibility in manufacturing by integrating shopfloor and process perception for mobile robot workers, Appl. Sci., vol. 11, no. 9, p. 3985, 2021.
A. S. M. Al-Obaidi, A. Al-Qassar, A. R. Nasser, A. Alkhayyat, A. J. Humaidi, and I. K. Ibraheem, Embedded design and implementation of mobile robot for surveillance applications, Indonesian J. Sci. Technol., vol. 6, no. 2, pp. 427–440, 2021.
Y. H. Jung, D. H. Cho, J. W. Hong, S. H. Han, S. B. Cho, D. Y. Shin, E. T. Lim, and S. S. Kim, Development of multi-sensor module mounted mobile robot for disaster field investigation, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLIII-B3-2022, pp. 1103–1108, 2022.
G. Lajkó, R. N. Elek, and T. Haidegger, Endoscopic image-based skill assessment in robot-assisted minimally invasive surgery, Sensors, vol. 21, no. 16, p. 5412, 2021.
Y. J. Mon, Vision robot path control based on artificial intelligence image classification and sustainable ultrasonic signal transformation technology, Sustainability, vol. 14, no. 9, p. 5335, 2022.
D. Zhang and Z. Guo, Mobile sentry robot for laboratory safety inspection based on machine vision and infrared thermal imaging detection, Secur. Commun. Netw., vol. 2021, p. 6612438, 2021.
A. Stateczny, K. Gierlowski, and M. Hoeft, Wireless local area network technologies as communication solutions for unmanned surface vehicles, Sensors, vol. 22, no. 2, p. 655, 2022.
P. A. Nugroho, Kontrol lampu gedung melalui WiFi esp8266 dengan web server lokal, J. Elektro Dan Informatika Swadharma, vol. 1, no. 2, pp. 1–11, 2021.
L. Withington, D. D. P. D. Vera, C. Guest, C. Mancini, and P. Piwek, Artificial neural networks for classifying the time series sensor data generated by medical detection dogs, Expert Syst. Appl., vol. 184, p. 115564, 2021.
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