This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem.
At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers of neural networks to accomplish the path planning task.
The NAO biomimetic robot respond quickly and correctly to the dynamic environment. The simulation experiments show that the deep neural network outperforms in dynamic and static environment than the conventional method.
A new method of deep learning based biomimetic robot path planning is proposed. The authors designed a multi-layer CNN which includes max-pooling layer and convolutional kernel. Then, the first and second layers features can be extracted by these kernels. Finally, the authors use the sparse auto encoder training algorithm to train the CNN so as to accomplish the path planning task of NAO robot.
This work was supported in part by the National Natural Science Foundation of China under Grants 61374127 and 61422301, the Natural Science Foundation of Heilongjiang Province of China under Grants F201428 and JC2015016, the Science and Technology Research of agricultural bureau in Heilongjiang province of China (HNK125B-04-03), the Scientific and Technology Research Foundation of Heilongjiang Education Department under Grants 12541061 and 12541592, Heilongjiang Postdoctoral Grant LRB2015-14 and the Doctoral Scientific Research Foundation of Heilongjiang Bayi Agricultural University under Grant XDB2014-12.
Lu, Y., Yi, S., Liu, Y. and Ji, Y. (2016), "A novel path planning method for biomimetic robot based on deep learning", Assembly Automation, Vol. 36 No. 2, pp. 186-191. https://doi.org/10.1108/AA-11-2015-108
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