Texture Dataset Collected by Tactile Sensors
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Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality [1-4] . As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time. We have limited our study to the machine perception/discrimination of various textures that can be sensed by sensors attached to a probe/stick touching the material surfaces via a single touch point. For this purpose, we developed two embedded systems, one that served as a vibrotactile stimulator system and one that recorded and classified the vibrotactile signals collected by its sensors. As the probe rubs against the surface of the textured material on the stimulator, the sensors attached to the probe capture the vibrotactile signals for real-time classification. The probe is 3D printed with high printing density so that it transmits the vibrations at its tip without distortion. Our study has been submitted under the title: “An Embedded System for Collection and Real-time Classification of a Tactile Dataset”, in which the data has been further elaborated and analyzed using the proposed signal feature extraction method and the Fourier transform as input to machine learning classifiers. We performed experiments both offline and on the proposed embedded platform in real-time. Based on the limited memory and performance budget of the embedded system used in this study, we have chosen the 3-dimensional accelerometer sensor (MMA-7660 from NXP Company [5]) and an electret condenser microphone (CMA-4544PF-W from CUI Company [6]) as the sources of recordings in our tactile dataset. We have used commercial off-the-shelf embedded boards and electrical components (AVR-based embedded boards, stepper motors, etc.) as well as our own designed and 3D printed mechanical components (including the rotating drum glued with different texture strips). The collected tactile dataset has 12 texture classes, including sandpapers of various grits, Velcro strips with various thicknesses, aluminum foil, and rubber bands of various stickiness. For each texture, 20 seconds of recordings are collected (corresponding to nearly five rotations of the drum). We used the sampling rate of 200Hz for the accelerometer to collect the vibration data and to 8kHz for the microphone to collect the sound data. We used the dataset to show that low-cost, highly accurate, and real-time tactile texture classification can be achieved on embedded systems using an ensemble of sensors, efficient feature extraction methods [7], and simple machine learning classifiers [8]. 1- J. C. Gwilliam, Z. Pezzementi, E. Jantho, A. M. Okamura, and S. Hsiao, “Human vs. robotic tactile sensing: Detecting lumps in soft tissue,” in 2010 IEEE Haptics Symposium, March 2010, pp. 21–28.2- S. Okamoto, H. Nagano, and HN. Ho, “Psychophysical Dimensions of Material Perception and Methods to Specify Textural Space,” In: Kajimoto H., Saga S., Konyo M. (eds) Pervasive Haptics. Tokyo: Springer Japan, 2016.3- W. Duchaine, “Why tactile intelligence is the future of robotic grasping,” in IEEE Spectrum Automaton. IEEE, 2016.4- A. Schmitz, Y. Bansho, K. Noda, H. Iwata, T. Ogata, and S. Sugano, “Tactile object recognition using deep learning and dropout,” in 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE, 2014, pp. 1044–1050.5- “3-axis orientation/motion detection sensor,” NXP Semiconductor, Document Number: MMA7660FC, 2012. [Online]. Available: https://www.nxp.com/docs/en/data-sheet/MMA7660FC.pdf6- “Electret condenser microphone sensor,” CUI Devices, Document Number: CMA-4544PF-W, 2013. [Online]. Available: https://www.mouser.com/datasheet/2/670/cma-4544pf-w-1309465.pdf7- E. Alpaydin, “Introduction to machine learning, third edition,” The MIT Press, Cambridge, 20148- M. Fernandez-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014. [Online]. Available: http://jmlr.org/papers/v15/delgado14a.html
借助微型嵌入式系统（embedded systems）实现材料属性的实时触觉感知（tactile perception）是一项极具挑战性的任务，同时对于机器人操控、假肢设备以及增强现实（augmented reality）等灵巧物体操控场景而言至关重要[1-4]。由于材料属性的心理物理维度涵盖了广泛的感知范畴，嵌入式触觉感知系统需要高效的信号特征提取与分类技术，以实时处理触觉传感器（tactile sensor）采集到的信号。 本研究仅聚焦于通过单点接触探针/杆连接的传感器所感知的各类纹理的机器感知与判别。为此，我们开发了两套嵌入式系统：一套用作振动触觉刺激器系统，另一套用于采集并分类其传感器所获取的振动触觉信号。当探针与刺激器上的纹理材料表面摩擦时，附着在探针上的传感器会采集振动触觉信号以进行实时分类。该探针采用高密度3D打印工艺制造，可无失真地传递尖端振动。 我们的研究以《用于触觉数据集采集与实时分类的嵌入式系统》为题提交，其中我们采用所提出的信号特征提取方法与傅里叶变换作为机器学习分类器的输入，对数据集进行了进一步的细化分析与处理。我们分别在离线环境与所提出的嵌入式平台上开展了实时实验。基于本研究中所用嵌入式系统有限的内存与性能预算，我们选用了恩智浦（NXP）公司的3轴加速度传感器（MMA-7660）[5]以及CUI公司的驻极体电容麦克风（CMA-4544PF-W）[6]作为触觉数据集的采集源。 我们采用了商用现货嵌入式开发板与电气元件（基于AVR的嵌入式开发板、步进电机等），以及自主设计并3D打印的机械结构（包括粘贴有不同纹理条带的旋转滚筒）。本次采集的触觉数据集包含12个纹理类别，涵盖不同粒度的砂纸、不同厚度的魔术贴（Velcro）条带、铝箔以及不同粘性的橡皮筋。针对每一种纹理，我们采集了20秒的录制数据（对应滚筒约5次旋转）。我们为加速度传感器设置200Hz的采样率以采集振动数据，为麦克风设置8kHz的采样率以采集音频数据。 我们利用该数据集验证了：借助多传感器集成、高效特征提取方法[7]以及简单的机器学习分类器[8]，可在嵌入式系统上实现低成本、高精度且实时的触觉纹理分类。 [1] J. C. Gwilliam, Z. Pezzementi, E. Jantho, A. M. Okamura, S. Hsiao. "Human vs. robotic tactile sensing: Detecting lumps in soft tissue", in 2010 IEEE Haptics Symposium, March 2010, pp. 21–28. [2] S. Okamoto, H. Nagano, HN. Ho. "Psychophysical Dimensions of Material Perception and Methods to Specify Textural Space", In: Kajimoto H., Saga S., Konyo M. (eds) Pervasive Haptics. Tokyo: Springer Japan, 2016. [3] W. Duchaine. "Why tactile intelligence is the future of robotic grasping", in IEEE Spectrum Automaton. IEEE, 2016. [4] A. Schmitz, Y. Bansho, K. Noda, H. Iwata, T. Ogata, and S. Sugano. "Tactile object recognition using deep learning and dropout", in 2014 IEEE-RAS International Conference on Humanoid Robots. IEEE, 2014, pp. 1044–1050. [5] "3-axis orientation/motion detection sensor", NXP Semiconductor, Document Number: MMA7660FC, 2012. [Online]. Available: https://www.nxp.com/docs/en/data-sheet/MMA7660FC.pdf [6] "Electret condenser microphone sensor", CUI Devices, Document Number: CMA-4544PF-W, 2013. [Online]. Available: https://www.mouser.com/datasheet/2/670/cma-4544pf-w-1309465.pdf [7] E. Alpaydin. "Introduction to machine learning, third edition", The MIT Press, Cambridge, 2014. [8] M. Fernandez-Delgado, E. Cernadas, S. Barro, and D. Amorim. "Do we need hundreds of classifiers to solve real world classification problems?" Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014. [Online]. Available: http://jmlr.org/papers/v15/delgado14a.html
