深度学习下盲人避撞路径导航方法研究
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安徽信息工程学院

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2020年安徽省自然科学基金面上项目(项目编号:2008085MF201)


Research on Navigation Method of Blind Collision Avoidance Path under Deep Learning
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Anhui Institute of Information Technology

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    摘要:

    传统导航方法只能检测出路径上存在的静止障碍物,无法检测出运动障碍物,为此提出基于深度学习的盲人避撞路径导航方法。收集语音信号,利用语音识别模型获取语音特征参数,根据语音特征参数识别出盲人输入的语音序列内容,确定盲人所要到达的目的地。构建障碍物检测模型,检测盲人所在位置与其目的地路径上障碍物的形状特征及其运动方向、速度,并计算初始位置与到达位置的距离。利用深度学习中的卷积神经网络规划出最优避撞路径,实现盲人避撞路径导航。实验结果显示,该方法检测出来的障碍物在x轴和y轴上的径向运动速度相差无几,可以实时跟踪监测障碍物的移动方向及运动速度。本研究方法所得速度与障碍物的实际运动速度基本一致,误差在0.2~0.4cm/s之间,且在测试时间为50min时,避障精准度达到了96.5%,能够实现最优避撞路径规划及导航。

    Abstract:

    Traditional navigation methods can only detect static obstacles on the path, but can not detect moving obstacles. Therefore, a collision avoidance path navigation method for blind people based on deep learning is proposed. The speech signal is collected, and the speech feature parameters are obtained by using the speech recognition model. According to the speech feature parameters, the content of the speech sequence input by the blind person is recognized, and the destination of the blind person is determined. The obstacle detection model is constructed to detect the shape features, moving direction and speed of the obstacles on the blind's location and their destination path, and calculate the distance between the initial position and the arrival position. The convolution neural network in deep learning is used to plan the optimal collision avoidance path and realize the blind collision avoidance path navigation. The experimental results show that the radial velocity of the obstacles detected by this method is almost the same on the x-axis and y-axis, and the moving direction and velocity of the obstacles can be tracked and monitored in real time. The speed obtained by this method is basically consistent with the actual speed of the obstacle, and the error is between 0.2 and 0.4cm/s. when the test time is 50min, the obstacle avoidance accuracy reaches 96.5%, which can achieve the optimal collision avoidance path planning and navigation.

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  • 收稿日期:2021-03-27
  • 最后修改日期:2021-05-18
  • 录用日期:2021-05-21
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