NRI: FND: Scalable, Customizable Sensory Solutions for Dexterous Robotic Hands

NRI：FND：适用于灵巧机械手的可扩展、可定制的感官解决方案

• 批准号: 1849417
• 负责人: Nitish Thakor
• 金额: $ 42.17万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849417&HistoricalAwards=false
• 关键词: NRI; FND; Scalable; Customizable; Sensory

This project aims to enhance the sense of touch for robotic hands. The main goal is to develop prosthetic hands with a sense of touch. The touch sensor's primary application is for upper limb amputees. The research team plans both fundamental research and its application. The types of applications of this kind of sensor include: humanoid robots for assistive work and elder-care, surgical robotics, underwater robotic manipulators and spacesuits. Additionally, educational initiatives including internships, training of under-represented minority, and research experiences and summer modules for high school students are planned through the Johns Hopkins Center for Talented Youth. The technical goal of the project is to build a highly scalable sensor design mimicking different tactile receptors in human skins and encode information from the sensors in a manner analogous to the neural activity of the tactile receptors. The sensor will encode the tactile information at multiple scales, firstly based on different receptor properties, and secondly based on neuron-like encoding of the sensor signals. This technique of encoding the neural activity, known as neuromorphic encoding, converts the sensor activity as an event stream, and from that data obtains finer features. The receptor based sensing along with the various neural network algorithms, for the first time, will provide an approach to texture and shape recognition and, as such, can also be useful for intelligent palpation and tactile perception by dexterous robotic hands.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目旨在增强机器人手的触觉。主要目标是开发具有触觉的假手。触摸传感器的主要应用是上肢截肢者。研究小组计划进行基础研究和应用。这种传感器的应用类型包括：用于辅助工作和老年人护理的人形机器人、外科机器人、水下机器人操纵器和宇航服。此外，约翰霍普金斯大学青年才俊中心还计划为高中生提供实习、培训少数族裔、研究经验和暑期课程等教育活动。该项目的技术目标是建立一个高度可扩展的传感器设计，模仿人类皮肤上不同的触觉感受器，并以类似于触觉感受器的神经活动的方式编码来自传感器的信息。传感器将对触觉信息进行多尺度编码，首先基于不同的受体特性，其次基于传感器信号的类神经元编码。这种编码神经活动的技术，被称为神经形态编码，将传感器活动转换为事件流，并从该数据中获得更精细的特征。基于受体的传感以及各种神经网络算法将首次为纹理和形状识别提供一种方法，因此，也可以用于灵巧机器人手的智能触诊和触觉感知。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback

• DOI: 10.1089/soro.2020.0016
• 发表时间: 2020-09-23
• 期刊: SOFT ROBOTICS
• 影响因子: 7.9
• 作者: Sankar, Sriramana;Balamurugan, Darshini;Thakor, Nitish
• 通讯作者: Thakor, Nitish

Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger

• DOI: 10.1109/sensors43011.2019.8956704
• 发表时间: 2019-10
• 期刊: 2019 IEEE SENSORS
• 作者: Sriramana Sankar;Alisa Brown;D. Balamurugan;Harrison H. Nguyen;Mark M. Iskarous;Talya Simcox;Deepesh Kumar;Andrei Nakagawa Silva;N. Thakor

Texture Discrimination Using a Neuromimetic Asynchronous Flexible Tactile Sensor Array with Spatial Frequency Encoding

使用具有空间频率编码的神经模拟异步柔性触觉传感器阵列进行纹理辨别

• DOI: 10.1109/ner49283.2021.9441136
• 发表时间: 2021
• 期刊: IEEE Neural Engineering
• 作者: Slepyan, Ariel;Sankar, Sriramana;Thakor, Nitish
• 通讯作者: Thakor, Nitish

Towards scalable soft e-skin: Flexible event-based tactile-sensors using wireless sensor elements embedded in soft elastomer

• DOI: 10.1109/biorob49111.2020.9224353
• 发表时间: 2020-11
• 期刊: 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
• 作者: Ariel Slepyan;N. Thakor

Unsupervised Learning and Adaptive Classification of Neuromorphic Tactile Encoding of Textures

• DOI: 10.1109/biocas.2018.8584702
• 发表时间: 2018-10
• 期刊: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)
• 作者: Mark M. Iskarous;Harrison H. Nguyen;Luke E. Osborn;Joseph L. Betthauser;N. Thakor