Department of Robotics and Mechatronics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.
Sensors (Basel). 2020 Jul 25;20(15):4121. doi: 10.3390/s20154121.
Autonomous dexterous manipulation relies on the ability to recognize an object and detect its slippage. Dynamic tactile signals are important for object recognition and slip detection. An object can be identified based on the acquired signals generated at contact points during tactile interaction. The use of vibrotactile sensors can increase the accuracy of texture recognition and preempt the slippage of a grasped object. In this work, we present a Deep Learning (DL) based method for the simultaneous texture recognition and slip detection. The method detects non-slip and slip events, the velocity, and discriminate textures-all within 17 ms. We evaluate the method for three objects grasped using an industrial gripper with accelerometers installed on its fingertips. A comparative analysis of convolutional neural networks (CNNs), feed-forward neural networks, and long short-term memory networks confirmed that deep CNNs have a higher generalization accuracy. We also evaluated the performance of the highest accuracy method for different signal bandwidths, which showed that a bandwidth of 125 Hz is enough to classify textures with 80% accuracy.
自主灵巧操作依赖于识别物体和检测其滑动的能力。动态触觉信号对于物体识别和滑动检测很重要。可以根据在触觉交互过程中在接触点处获取的信号来识别物体。使用振动触觉传感器可以提高纹理识别的准确性，并预先防止抓取物体的滑动。在这项工作中，我们提出了一种基于深度学习 (DL) 的同时进行纹理识别和滑动检测的方法。该方法可以检测非滑动和滑动事件、速度并区分纹理——所有这些都在 17 毫秒内完成。我们使用安装在指尖上的加速度计的工业夹爪对三个物体进行了评估。对卷积神经网络 (CNN)、前馈神经网络和长短时记忆网络的比较分析证实，深度 CNN 具有更高的泛化准确性。我们还评估了针对不同信号带宽的最高精度方法的性能，结果表明带宽为 125 Hz 足以以 80%的准确率对纹理进行分类。
