一种触觉感知与脑启发的触觉传感系统

触觉智能感知是当前研究的热点问题之一.然而,大规模触觉数据集的缺乏限制了机器人触觉感知领域的发展,解决问题的关键在于构建覆盖手掌的高时空分辨率触觉压力传感器系统.对此,构建一种脑启发的触觉传感系统(BITSS),以高时空分辨率对触觉压力信息进行获取,并实现基于脉冲事件的触觉感知.受皮肤触觉感受器启发,BITSS使用神经形态模型对触感压力信号进行脉冲编码,实现两种触觉感受器神经元的模拟.实验结果表明,BITSS模拟的神经放电活动可以解码出抓握状态的低维空间.在10种日常物体的分类任务中,基于脉冲事件的分类器分类精度达到94%,具有较快的执行速度,并验证了BITSS对触感压力信号的时空编码能力.

A tactile perception and brain-inspired tactile sensing system

Tactile intelligent perception is one of the hot issues in current researches. However, the lack of large-scale tactile data sets limits the development of the field of tactile perception. The key to solving the problem is to build a high-temporal-resolution tactile pressure sensor system that covers the palm. This paper constructs a brain-inspired tactile sensing system(BITSS), which acquires tactile pressure information with high spatiotemporal resolution and realizes tactile perception based on spike events. Inspired by the skin tactile receptors, the BITSS uses the neuromorphic model to encode spike the tactile pressure signals and realizes the simulation of two types of tactile receptor neurons. The experimental results show that the neuroelectric activity simulated by the BITSS can decode the low-dimensional space of the grasping state. In the classification tasks of ten daily objects, we provide a spike-based Bayesian classifier with a classification accuracy of 94% and a fast execution speed. The above results verify the spatiotemporal coding ability of the BITSS for tactile pressure signals.