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实现材料属性的实时触觉感知，既是一项极具挑战性的任务，同时在机器人操作、假肢操控与增强现实等灵巧物体操控场景中具备至关重要的研究价值[1-4]。 由于材料属性的心理物理学维度涵盖了广泛的感知范畴，嵌入式触觉感知系统需借助高效的信号特征提取与分类技术，方可实时处理触觉传感器采集到的信号。本研究仅针对通过单点接触式探针上的传感器采集得到的各类纹理，开展机器感知与判别相关研究。 为此，本研究开发了两套嵌入式系统：其一为振动触觉刺激系统，其二则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，附着于探针的传感器将采集振动触觉信号，用于实时分类。本探针采用高打印密度的3D打印工艺制作，确保其尖端可无失真地传递振动信号。 本研究已以《用于触觉数据集采集与实时分类的嵌入式系统》为题投稿，该文稿中针对本数据集采用了所提出的信号特征提取方法，并以傅里叶变换（Fourier transform）结果作为输入，送入机器学习分类器进行数据的深入阐释与分析。本研究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。 鉴于本研究所用嵌入式系统的内存与性能预算有限，本触觉数据集选用了三维加速度传感器（3-dimensional accelerometer sensor，来自恩智浦（NXP）公司的MMA-7660[5]）与驻极体电容麦克风（electret condenser microphone，来自CUI公司的CMA-4544PF-W[6]）作为采集设备。本研究采用了商用现成嵌入式板卡与电子元器件（如基于AVR的嵌入式板卡、步进电机等），同时使用了自主设计并3D打印的机械结构，包括粘贴有不同纹理条带的旋转滚筒。 本研究采集的触觉数据集包含12类纹理，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。每类纹理均采集20秒的录音数据，对应滚筒旋转约五圈。本研究以200Hz的采样率采集加速度传感器的振动数据，以8kHz的采样率采集麦克风的音频数据。 本研究借助该数据集验证了：依托多传感器融合、高效特征提取方法[7]与简易机器学习分类器[8]，可在嵌入式系统上实现低成本、高精度的实时触觉纹理分类任务。 1- J. C. Gwilliam、Z. Pezzementi、E. Jantho、A. M. Okamura与S. Hsiao，《人机触觉感知：检测软组织中的肿块》，收录于2010年IEEE触觉研讨会（IEEE Haptics Symposium），2010年3月，第21-28页。 2- S. Okamoto、H. Nagano与HN. Ho，《材料感知的心理物理学维度与纹理空间表征方法》，收录于Kajimoto H.、Saga S.、Konyo M.主编《普适触觉技术》，东京：日本斯普林格出版社，2016年。 3- W. Duchaine，《为何触觉智能是机器人抓取技术的未来》，收录于IEEE Spectrum Automaton，IEEE，2016年。 4- A. Schmitz、Y. Bansho、K. Noda、H. Iwata、T. Ogata与S. Sugano，《基于深度学习与Dropout的触觉物体识别》，收录于2014年IEEE-RAS仿人机器人国际会议（IEEE-RAS International Conference on Humanoid Robots），IEEE，2014年，第1044-1050页。 5- 《三轴姿态/运动检测传感器》，恩智浦半导体（NXP Semiconductor），文档编号：MMA7660FC，2012年。[在线]. 可获取：https://www.nxp.com/docs/en/data-sheet/MMA7660FC.pdf 6- 《驻极体电容麦克风传感器》，CUI器件公司（CUI Devices），文档编号：CMA-4544PF-W，2013年。[在线]. 可获取：https://www.mouser.com/datasheet/2/670/cma-4544pf-w-1309465.pdf 7- E. Alpaydin，《机器学习导论（第三版）》，麻省理工学院出版社（The MIT Press），剑桥，2014年。 8- M. Fernandez-Delgado、E. Cernadas、S. Barro与D. Amorim，《我们是否需要数百个分类器来解决现实世界的分类任务？》，《机器学习研究期刊》（Journal of Machine Learning Research），第15卷，第3133-3181页，2014年。[在线]. 可获取：http://jmlr.org/papers/v15/delgado14a.html
