Collision Avoidance System Synthesis for a Group of Robots in Unsupervised Learning Paradigm

Dotsenko, Anton

doi:10.17587/mau.21.420-427

Mehatronika, avtomatizaciâ, upravlenie

2020

DOI: 10.17587/mau.21.420-427

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Collision Avoidance System Synthesis for a Group of Robots in Unsupervised Learning Paradigm

Anton Dotsenko

Abstract: Collision avoidance is very important problem in the domain of multi-robot interaction. In this paper we propose a new approach o… Show more

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2024

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Cited by 1 publication

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Method of Motion Path Planning Based on a Deep Neural Network with Vector Input

Hamdan,

Medvedev,

Pshikhopov

2024

Mehatronika, avtomatizaciâ, upravlenie

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The article deals with the problem of path planning in a two-dimensional environment based on deep learning neural networks. Deep neural networks require large amounts of data and place high computational requirements on computing tools. The lack of sufficient data leads to a decrease in the accuracy of the neural network, and high computational requirements at the learning stage limit the use of this technology in engineering practice. In this paper, the forms of representation of the environment for the input of a neural network are studied. Vector form allows to reduce the amount of information supplied to the input of a neural network, but it leads to the need to use more complex neural networks. In this article, a combined form of representation is proposed, including a vector global and local map layout. The vector part of the map includes the position of the robot, the position of the target point and a description of obstacles. The local raster map describes the area closest to the robot. Using numerical research, the effectiveness of this form of data representation for a precise neural network is shown, compared with the raster representation of the map. In this article, two structures of neural networks are studied, one of which uses 8 possible directions of movement, and the other uses 3 possible directions of movement. It is shown that when using 3 possible directions, the cycling of trajectories planned by the neural network is eliminated, which leads to an increase in accuracy.

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Method of Motion Path Planning Based on a Deep Neural Network with Vector Input

Hamdan,

Medvedev,

Pshikhopov

2024

Mehatronika, avtomatizaciâ, upravlenie

View full text Add to dashboard Cite

show abstract

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Collision Avoidance System Synthesis for a Group of Robots in Unsupervised Learning Paradigm

Abstract: Collision avoidance is very important problem in the domain of multi-robot interaction. In this paper we propose a new approach o… Show more

Cited by 1 publication

References 29 publications

Method of Motion Path Planning Based on a Deep Neural Network with Vector Input

Method of Motion Path Planning Based on a Deep Neural Network with Vector Input

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