Deep Vibro-Tactile Perception for Simultaneous Texture Identification, Slip Detection, and Speed Estimation

Sensors

Yerkebulan Massalim¹  Zhanat Kappassov¹  HuseyinAtakan Varol¹ 

[1] Department of Robotics and Mechatronics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;

关键词: tactile sensing;    slip detection;    texture identification;    deep learning;    convolutional neural networks;    long short-term memory;   

DOI  :  10.3390/s20154121

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown