A Neural Network Based Mobile Robot Autonomous Tracking System

Zhao, Teng; Wang, Ying

doi:10.1109/secon.2018.8479118

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…It should be noted that these values, changed by a specific figure, are the input parameters of the control algorithm. And the mobile robot position change commands are directly transmitted to the drive through the input and output interface module to the control motors [10], enabling the robot to move along a safe trajectory are the outputs of the control algorithm. Fig.…”

Section: Methodsmentioning

confidence: 99%

Application of combination of neural networks and fuzzy logic in planning of mobile robot motion in unknown environment

Nemeiksis

Osadčuks

2019

Engineering for Rural Development

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The significance of automated control systems to contemporary society is profound, as large-scale industrial robots are used in industrial plants, autonomous vacuum cleaners are applied at home, and autonomous cars are driven in the streets. Regardless of the nature of the robot, one of its main tasks is to do no harm to the person and the environment, and that is the greatest problem of robotics-orientation. Therefore, the problem of orientation of autonomous robots is being solved by a robot exploring the environment, remembering the space and time-varying environmental changes. On that ground, the task of this article is to develop such scientific researches, which define the mobile robot motion in an unknown environment, based on fuzzy logic and the analysis of neural networks, setting an aim to work out methods for developing intellectual systems for planning a mobile robot motion fuzzy logic and neural networks to ensure that the robot performs the planned and adjusted on the way safe trajectory in an environment with unknown obstacles. Therefore, the entire study in the article is aimed at the analysis of fuzzy logic that is analysed as the entirety of the mathematical description methods of fuzzy sets with the formalization of logical conclusions from the fuzzy assumptions, as in this case the decision-making mechanism always allows the generation of the robot's responsive motions caused by the appearance of obstacles in its trajectory; as well as the analysis of neural networks, which links between neurons determine the complexity and flexibility of the operation of the entire neural network, by addressing the problem of planning a mobile robot motion in an unknown environment and which basically depends upon the level of specific network training. As a result of the study, the method of a mobile robot motion in an unknown dynamic environment was created by using a multi-agent system, involving a combination of neural networks and fuzzy blocks.

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Section: Methodsmentioning

confidence: 99%