Robot route planning method by employing improved convolutional neural network based on K mean value

A convolutional neural network and path planning technology, which is applied in the field of robot path planning based on K-means improved convolutional neural network, can solve the problems of large number of convolutional neural network parameters, gradient dispersion, and parameters that cannot be learned to optimal values. , to achieve fast training speed, improve accuracy, and solve the effect of gradient dispersion problem

Inactive Publication Date: 2017-05-10
DONGHUA UNIV
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[0005] The technical problem to be solved by the present invention is that the existing convolutional neural network has a

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  • Robot route planning method by employing improved convolutional neural network based on K mean value
  • Robot route planning method by employing improved convolutional neural network based on K mean value
  • Robot route planning method by employing improved convolutional neural network based on K mean value

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[0028] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0029] The invention provides a robot path planning method based on K-means improved convolutional neural network, which adopts weight sharing and local connection to reduce the number of parameters to be learned and improve efficiency.

[0030] Such as figure 1 As shown, each layer and the previous layer are locally connected instead of fully connected. The nerve cells of the first layer are connected to the three nerve cells of the 1-1 laye...

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Abstract

The present invention provides a robot route planning method by employing an improved convolutional neural network based on a K mean value. The method comprises the following three steps: the step 1, establishing a K mean value clustering algorithm model and a BP network; the step 2, inputting the initial patrol point position of the robot into the K mean value clustering algorithm model; and the step 3, taking the output result of the K mean value clustering model as the input value of the BP network, and performing training, wherein the output value of the BP network is namely the optimal robot patrol route. According to the method provided by the invention, the first layer of the network employs K mean value unsupervised learning, and the second layer of the network employs the BP algorithm supervised learning to increase the correlation of the features obtained through the training and the actual tasks, improve the accuracy, reduce the parameters required training and solve the gradient dispersion problem. The improved algorithm is applied to the robot route plan and has high accuracy and faster training operation speed compared to the main algorithms about the experiment results.

Description

technical field [0001] The invention belongs to the technical field of automatic control, in particular to a robot path planning method based on K-means improved convolutional neural network. Background technique [0002] In recent years, deep learning has received more and more attention, and it is regarded as a key link leading to artificial intelligence. As a paradigm of deep learning, convolutional neural networks have been greatly promoted and developed. In 2012, the team led by Krizewski used the deep convolutional neural network to emerge in the world's top event image network large-scale visual recognition challenge, broke the record, and the final score far exceeded the results of other algorithms. In many current recognition tasks, convolutional neural networks have irreplaceable advantages compared with other human algorithms of the same recognition level. Therefore, convolutional neural networks are of epoch-making significance for solving deep learning problem...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 李文齐吴国文
Owner DONGHUA UNIV
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