NRI: FND: Scalable Multimodal Tactile Sensing for Robotic Manipulators in Manufacturing

NRI：FND：用于制造中机器人操纵器的可扩展多模式触觉传感

• 批准号: 1734557
• 负责人: Matei Ciocarlie
• 金额: $ 75万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734557&HistoricalAwards=false
• 关键词: NRI; FND; Scalable; Multimodal; Tactile

The research focuses on sensors and software to enable robots to have an artificial sense of touch. Current robot hands and grippers rarely have the ability to sense their environments; they operate by precisely repeating pre-programmed motions. This prevents the use of robots in applications where the state of the world is unknowable in advance, since such use requires a robot to sense and react to unexpected events. The project will design novel and sophisticated touch sensors and equip robot hands with them. The sensors will include multiple types of touch sensors and integrate them to detect different types and characteristics of contacts: fast versus slow, transitory versus maintained, etc. These data will be interpreted by software algorithms that learn how to make use of the newly acquired touch data. This approach takes its inspiration from the human hand, which is also equipped with multiple types of tactile sensing elements relaying information to the nervous system. The proximate goal of the work is to build tools for material handling in cluttered environments, a task that can enable e-commerce and supply chains to run more efficiently, help manufacturers become more efficient and competitive, and reduce injuries to workers by assisting with repetitive tasks that are known to cause injuries.Novel sensor design, computation and planning will be integrated to endow robots with a sense of touch, thereby enabling manipulation in cluttered environments. At the hardware level, the sensors will combine piezoelectric and resistive strain sensing to capture both transient, high-frequency responses and absolute strain measurements. Sensor sheets will be stacked in multiple sensing layers to provide a rich signal set that departs from the "one location, one taxel" approach that is normally taken and maximum use will be made of the rich data by combining model-based and data-driven approaches to define motor control primitives. These will be combined to implement algorithms for motor control and planning that use the tactile data in a target task: bin-picking and kitting in manufacturing. The overall goal is a compact, multi-modal, scalable tactile sensing system for robotic manipulators, along with the low-level motor skills and high level planning algorithms that use tactile data for manipulating in clutter.

研究重点是传感器和软件，使机器人具有人工触觉。目前的机器人手和抓取器很少有能力感知它们的环境;它们通过精确地重复预先编程的动作来操作。这就阻止了机器人在世界状态事先不可知的应用中的使用，因为这种使用需要机器人对意外事件进行感知和反应。该项目将设计新颖而复杂的触摸传感器，并为机器人配备这些传感器。 传感器将包括多种类型的触摸传感器，并将它们集成以检测不同类型和特征的接触：快速与慢速，暂时与维持等。这种方法的灵感来自于人手，人手也配备了多种类型的触觉传感元件，将信息传递到神经系统。这项工作的近期目标是构建在混乱环境中进行物料搬运的工具，这项任务可以使电子商务和供应链更有效地运行，帮助制造商提高效率和竞争力，并通过协助重复性任务来减少工人的伤害，这些重复性任务已知会导致伤害。新颖的传感器设计，计算和规划将被集成，以赋予机器人触觉，从而能够在杂乱的环境中进行操纵。在硬件层面，传感器将结合联合收割机压电和电阻应变传感，以捕捉瞬态，高频响应和绝对应变测量。传感器片将被堆叠在多个感测层中，以提供丰富的信号集，该信号集与通常采用的“一个位置，一个位置”方法不同，并且将通过结合基于模型的方法和数据驱动的方法来最大限度地利用丰富的数据来定义运动控制原语。 这些将被结合起来，以实现运动控制和规划的算法，这些算法在目标任务中使用触觉数据：制造中的装箱和装配。总体目标是一个紧凑的，多模态的，可扩展的触觉传感系统的机器人操纵器，沿着低层次的运动技能和高层次的规划算法，使用触觉数据在混乱中操纵。

项目成果

Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter

• DOI: 10.1007/s10514-020-09907-y
• 发表时间: 2020-02
• 期刊: Autonomous Robots
• 影响因子: 3.5
• 作者: Bohan Wu;Iretiayo Akinola;Abhi Gupta;Feng Xu;Jacob Varley;David Watkins-Valls;P. Allen

Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

• DOI: 10.1109/iros40897.2019.8968263
• 发表时间: 2019-03
• 期刊: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Bohan Wu;Iretiayo Akinola;P. Allen

A Sensorized Multicurved Robot Finger With Data-Driven Touch Sensing via Overlapping Light Signals

• DOI: 10.1109/tmech.2020.2975578
• 发表时间: 2020-10-01
• 期刊: IEEE-ASME TRANSACTIONS ON MECHATRONICS
• 影响因子: 6.4
• 作者: Piacenza, Pedro;Behrman, Keith;Ciocarlie, Matei
• 通讯作者: Ciocarlie, Matei

Tri-modal thin-film flexible electronic skin to augment robotic grasping

三模态薄膜柔性电子皮肤增强机器人抓取能力

• DOI: 10.1109/memsys.2018.8346698
• 发表时间: 2018
• 期刊: MEMS 2018
• 作者: Yu, Caroline;Cavallari, Marco R.;Kymissis, Ioannis
• 通讯作者: Kymissis, Ioannis

Sheet-based flexible technologies for mechanical sensing

用于机械传感的基于片材的柔性技术

• DOI: 10.1109/ifetc.2018.8583883
• 发表时间: 2018
• 期刊: 2018 International Flexible Electronics Technology Conference (IFETC
• 作者: Kymissis, Ioannis;Yu, Caroline;Hsu, Yu-Jen;Piacenza, Pedro;Hannigan, Emily;Ciocarle, Matei;Allen, Peter
• 通讯作者: Allen, Peter