深度学习下盲人避撞路径导航方法研究

Deep learning-based navigation path planning with collision avoidance for the blind

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0.2~0.4 cm/s之间,且在测试时间为50 min时,避障精准度达到96.5%,能够实现最优避撞路径规划及导航.

Moving obstacles, unlike stationary ones, cannot be located or avoided by traditional navigation technologies.To address this, a collision avoidance navigation path planning strategy for blind people based on deep learning is proposed.First, a speech recognition system is used to collect speech signal and sort out the speech feature parameters, which is then analyzed to obtain the speech sequence input thus recognize the destination.Second, an obstacle detection model is constructed to detect the edge features as well as moving directions and velocities of the obstacles on the path to destination.Then a convolutional neural network of deep learning is exploited to plan the optimal path with collision avoidance.Finally, experiments are conducted and the results show that the radial velocity of the moving obstacles detected by this model is consistent with actual conditions, specifically, when the actual speed is 33.6 cm/s, the detected speed error is in the range of 0.2-0.4 cm/s, and the accuracy of obstacle avoidance reaches 96.5% when the test time lasts 50 min.It can be concluded that the proposed strategy can realize the optimal path planning and navigation with collision avoidance for the blind people.