The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change

Ignatov, Alexander; Ivchenko, Valeriy; Krug, Petr; Matyukhina, Ekaterina; Pavelyev, Sergey

doi:10.4028/www.scientific.net/amm.851.477

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…With the development of science and technology, artificial intelligence technology [1] has gradually developed in various fields, constantly changing human life. As the result of artificial intelligence technology, robot [2] is gradually developed and applied by all countries in the world, and progressively enters the families of ordinary people, bringing convenience and superior performance to the lives.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Positioning Method of Indoor Mobile Robot Using Wireless Network

2020

IJOMAM

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The objective of this study is to realize the monitoring, positioning and route planning of indoor mobile robot, so that the brain of indoor mobile robot can form the route planning map in different environments, and finally achieve the best route navigation of indoor mobile robot. In this study, the ultra-wideband (UWB) wireless network location method of indoor mobile robot is proposed with laser pulse ranging method to measure the positioning distance of indoor mobile robots. Secondly, the trilateral positioning method is used to measure the position of the starting node to several known ending nodes, to estimate the position of the starting node of the indoor mobile robot. Finally, gyroscopes and odometers are used to measure the rectangular and U-shaped motion of indoor mobile robots. The results show that the sampling frequency of 50Hz UWB wireless network is used to collect offline information about the mobile indoor robot. After the information data is processed, the measurement noise covariance matrix can be obtained, and finally, the expected target can be obtained, and the positioning distance measurement of indoor mobile robot can be realized. When the robot glides along the rectangular road with a starting point of (2.48, 2.15) and the U-shaped road with a starting point of (7.16, 6.31) at a constant speed, its UWB measurement values basically jump near the route. At the same time, there will be noise, and the density of the numerical results is relatively low, which means that the UWB can find a stable direction for the indoor mobile robot, but it is unable to measure the path angle deviation of the robot. The deviation angles of the robot rectangular route are 90°, 180°, 270° and 360° respectively. The deviation angle error of gyroscope will increase with the increase of robot moving time. The deviation angle errors of the rectangular and U-shaped routes measured by the odometer are kept around the 0 reference line, which indicates that compared with the gyroscope, the odometer can correct the angle by the specific calculation method when the angle deviation is large, so as to keep the error around the 0 reference line. The results can provide a practical basis for indoor mobile robot to monitor and locate accurately through wireless network.

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Section: Introductionmentioning

confidence: 99%

Monitoring and Positioning Method of Indoor Mobile Robot Using Wireless Network

2020

IJOMAM

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Using artificial intelligence for effective decision-making in corporate governance under conditions of deep uncertainty

Volosova

Матюхина

2020

SHS Web of Conf.

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The article deals with research related to the use of artificial intelligence technologies for effective decision-making in corporate governance under conditions of deep uncertainty. To process uncertainty, it is proposed to use the cognitive capabilities of artificial intelligence. Cognitivism can be used to implement intuitive, psychological and other components of the internal mental activity of a person when making decisions. These capabilities allow one to make informed decisions and predict the consequences of these decisions. To study the properties of deep uncertainty, the authors suggest using a tensor model. The tensor model of deep uncertainty makes it possible to study additional properties of uncertainty that are not available in traditional models, such as Bayesian formalism, Dempster-Shafer theory, fuzzy sets, a method based on certain factors (Stanford formalism), and others. The use of the tensor model allows one to study the spatial model of uncertainty, real and imaginary values of uncertainty, as well as uncertainty invariants with respect to various transformations of the coordinate system.

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The Technologies for Remote Reconfiguration of Artificial Intelligence of Robotic Systems in Case of Mission or Driving Conditions Change

Cited by 2 publications

References 5 publications

Monitoring and Positioning Method of Indoor Mobile Robot Using Wireless Network

Monitoring and Positioning Method of Indoor Mobile Robot Using Wireless Network

Using artificial intelligence for effective decision-making in corporate governance under conditions of deep uncertainty

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