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A Deep Learning-based Visual Perception Approach for Mobile Robots

机译:基于深度学习的移动机器人视觉感知方法

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In this work, a deep learning-based approach was developed for the visual path perception of mobile robots, combined with computer vision technology. An experimental platform of differential wheeled mobile robots and a LabView upper computing platform to realize the basic motion control and test the actual effect of the pre-trained DNN(Deep Neural Network) model was built. The training method of DNN, including the acquisition of data set, Deep Neural Network structure and training program was given out. Experimental results demonstrate the feasibility of applying deep learning to mobile robot's visual perception of path. It has a reference significance for improving the intelligent level of mobile robots.
机译:在这项工作中,结合计算机视觉技术,针对移动机器人的视觉路径感知开发了一种基于深度学习的方法。建立了差动轮式移动机器人实验平台和LabView上位机平台,实现了基本的运动控制,并测试了预训练DNN(Deep Neural Network)模型的实际效果。提出了DNN的训练方法,包括数据集的获取,深度神经网络的结构和训练程序。实验结果证明了将深度学习应用于移动机器人对路径的视觉感知的可行性。对于提高移动机器人的智能水平具有参考意义。

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