Magnetic field aided positioning technology of roadheader cutting part based on one-dimensional convolution neural network

Abstract

In order to solve the problem of visual positioning failure caused by the contilever roadheader when the cutting part is blocked by the fuselage or the dust is serious,this paper proposed an auxiliary positioning method based on one-dimensional convolutional neural network (1D-CNN).The network parameters were obtained by taking the intensity component of the magnetic field and the pose data obtained by binocular stereo vision technology as training data.The experimental results show that the 1D-CNN can predict the trajectory of the cutting part better,and the prediction accuracy of the pitch angle and yaw angle of the space angle is more than 99%.This method can effectively predict the spatial pose information of the cutting part of the roadheader.Compared with the BP fully connected neural network,it has the advantages of automatic feature extraction and avoiding overfitting,and puts forward a new idea for the positioning of the cutting part of the roadheader.

Keywords

• other disciplines of mining engineering; boom-type roadheader; convolutional neural network; magnetic field positioning; pose measurement