基于一维卷积神经网络的掘进机截割部磁场辅助定位技术
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国家自然科学基金(62175059); 河北省创新能力提升计划资助项目(20540302D)


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

    为了解决悬臂式掘进机当截割部被机身遮挡或粉尘比较严重时引发的视觉定位失效问题,以磁场强度分量和双目立体视觉技术获取的位姿数据作为训练数据,获得网络参数,提出一种基于一维卷积神经网络(1D-CNN)的辅助定位方法。结果表明,1D-CNN对截割部轨迹预测较好,空间角度俯仰角、偏航角的预测精度达到99%以上,总体精度满足悬臂式掘进机位姿的测量要求。所提方法可以有效预测掘进机截割部的空间位姿信息,与BP全连接神经网络相比,具有能自动提取特征、避免过拟合的优点,为掘进机截割部定位提出了新思路。

    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.

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周红旭,孙海军,张 雷,王华英.基于一维卷积神经网络的掘进机截割部磁场辅助定位技术[J].河北科技大学学报,2022,43(3):231-239

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  • 收稿日期:2022-03-27
  • 最后修改日期:2022-05-14
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  • 在线发布日期: 2022-07-08
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