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; 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.

该研究的重点是传感器和软件，使机器人能够具有人为的触觉。当前的机器人手和握手很少能够感知其环境。它们通过精确重复预编程的运动来运行。这样可以防止在世界状态不可知的应用程序中使用机器人，因为这种使用需要机器人来感知和对意外事件的反应。该项目将设计小说且精致的触摸传感器，并配备机器人手。 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触点的不同类型和特征：快速与缓慢，暂时性与维护等。这些数据将由软件算法解释，这些软件算法学习如何利用新获取的触摸数据。这种方法从人的手中汲取灵感，它也配备了多种类型的触觉感应元素，将信息传达给神经系统。这项工作的直接目标是在混乱的环境中建造工具，该任务可以使电子商务和供应链更有效地运行，帮助制造商变得更有效，更有效，更有竞争力，并减少对工人的伤害，通过促进重复性任务，以造成伤害。混乱的环境。在硬件级别上，传感器将结合压电和电阻应变传感，以捕获瞬时，高频响应和绝对应变测量值。传感器表将堆叠在多个传感层中，以提供富含的信号集，该集合与通常采用的“一个位置”方法偏离“一个位置，一个符号”方法，并通过将基于模型的基于模型的和数据驱动的方法结合来​​定义电动机控制原始素，从而最大程度地使用。 这些将结合起来，以实现用于电机控制和计划的算法，这些算法在目标任务中使用触觉数据：制造中的bin选择和套件。总体目标是用于机器人操纵器的紧凑，多模式，可扩展的触觉传感系统，以及使用触觉数据来操纵混乱的低级运动技能和高级计划算法。