Geodesic
Intelligent system for testing robotic complexes
News of the Kabardin-Balkar scientific center of RAS, no. 6 (2021), pp. 43-49.

Voir la notice de l'article provenant de la source Math-Net.Ru

The paper considers the problem of developing an intelligent testing system for robotic systems based on sigma-pi neural networks. On production lines where industrial robots are used, the task of testing them for performance is urgent. There are two main ways to solve this problem: routine checks of robotic systems or constant observation of the operator at the robotic line. This paper presents an intelligent system built on the basis of sigma-pi neural networks, which will be able to solve a similar problem using readings from sensors located at different nodes of the robot. A neural network trained according to the algorithm considered in the work can continuously monitor the state of robots on the production line and make a decision to stop the line in case of suspicion of a breakdown. As an example of the operation of a sigma-pi neural network in this work, an example is provided based on 5 input data, that is, data from 5 sensors, normalized according to the principle "there is a signal" or "there is no signal".
Keywords: :sigma-pi neural networks, control problem, intelligent testing, robotic systems, neurocontrol. 
@article{IZKAB_2021_6_a2,
     author = {R. A. Zhilov},
     title = {Intelligent system for testing robotic complexes},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {43--49},
     publisher = {mathdoc},
     number = {6},
     year = {2021},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2021_6_a2/}
}
TY  - JOUR
AU  - R. A. Zhilov
TI  - Intelligent system for testing robotic complexes
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2021
SP  - 43
EP  - 49
IS  - 6
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2021_6_a2/
LA  - ru
ID  - IZKAB_2021_6_a2
ER  - 
%0 Journal Article
%A R. A. Zhilov
%T Intelligent system for testing robotic complexes
%J News of the Kabardin-Balkar scientific center of RAS
%D 2021
%P 43-49
%N 6
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2021_6_a2/
%G ru
%F IZKAB_2021_6_a2
R. A. Zhilov. Intelligent system for testing robotic complexes. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2021), pp. 43-49. http://geodesic.mathdoc.fr/item/IZKAB_2021_6_a2/

[1] P. R. Belyanin, Industrial robots and their applications. Robotics for mechanical engineering, Mashinostroyeniye, Moscow, 1983, 311 pp. (In Russian)

[2] A. A. Bobtsov, V. I. Boykov, S. V. Bystrov, V. I. Grigoriev, Actuators and systems for micromovements, ITMO, Saint Petersburg, 2011, 131 pp. (In Russian)

[3] Ya. Z. Tsypkin, Adaptation and training in automatic systems, Nauka, Moscow, 1968, 400 pp. (In Russian)

[4] E. P. Popov, Robotics and flexible production systems, Nauka, Moscow, 1987, 192 pp. (In Russian)

[5] Automation, robotics and flexible production systems for forging and stamping production, eds. K.I. Vasiliev, A.M. Smirnov, E.N. Sosenushkin and others, Izdatel'stvo TNT, Oskol, 2009, 484 pp. (In Russian)

[6] A. S. Mikhailova, A. S. Kurushin, “Application of neural networks in adaptive robotics”, Collection of articles of the XXVIII international scientific and practical conference, Moscow, 2020, 77–80 (In Russian)

[7] L. A. Lyutikova, A. V. Timofeev, V. V. Sgurev, V. I. Yotsov, “Development and application of multivalued logics and network flows in intelligent systems”, Proceedings of SPIIRAS, 2005, no. 2, 114–126 (In Russian)

[8] A. V. Timofeev, T. M. Kosovskaya, “Neural network methods of logical description and recognition of complex patterns”, Proceedings of SPIIRAS, 2013, no. 27, 144–155 (In Russian)