I-Corps: Robotic Hands for warehousing & Automation

自动化产品处理的典型客户是具有高吞吐量的仓储企业，其产品的形状和尺寸范围很广。这些仓库为在消费电子产品、食品配送和制药等领域运营的在线企业提供存储和履行物流。例如亚马逊、塔吉特和CVS。大约有172000名仓库工人参与手工搬运库存。这些仓库面临着持续但波动的发货订单需求，这些需求可能会受到季节性假期、时尚趋势甚至一周中哪一天的影响。这些仓库目前的解决方案是，当需求增长时，雇佣更多的员工来挑选和包装这些包裹。自动化这些挑选订单的任务还将解决季节性劳动力波动和恶劣的工作条件的问题。这个研究小组开发了一种获得专利的机器人手，它将彻底改变挑选和包装。拟议的技术将通过更快地包装和运输产品来创造价值，同时消除员工在压力环境中因重复任务而造成的伤害。机械手技术的能力通过两个关键进步来实现：自适应机械设计和高可靠性触觉传感器。手的机械原理基于特殊设计的多自由度柔性关节，使手指能够被动地针对对象塑造自己，简化了控制，减少了抓住对象所需的信息，降低了成本，并提供了温和的触摸。触觉感应技术帮助手适应物体的形状(特别是对于轻物体)，并检测错误。这些设备比竞争对手的技术更敏感，也更容易制造。该团队将致力于为产品搬运市场开发这种机器人手技术。目前处理物体的黄金标准是使用真空夹持器。这些抓手的设计和实现都很简单，但它们可以处理的产品范围相当有限。此外，最先进的自动化系统使用专门的夹爪，如平口夹爪，这些夹爪只执行一系列狭窄的任务，需要专家选择合适的夹爪(这是一个昂贵且缓慢的过程)，如果产品线发生变化，必须更换。

A typical customer for automatic product handling is a warehousing business with high throughput of products with a wide range of shapes and sizes. These warehouses provide storage and fulfillment logistics for online businesses operating in areas such as consumer electronics, food distribution, and pharmaceuticals. Examples include Amazon, Target, and CVS. There are approximately 172,000 warehouse workers involved in moving inventory by hand. These warehouses face a constant but fluctuating demand for shipping orders that can be influenced by seasonal holidays, fashion trends or even day of the week. The current solution for these warehouses is to hire additional staff to pick-and-pack these packages when the demand grows. Automating these order-picking tasks would also solve issues of seasonal labor fluctuations, and the poor working conditions. This research team has developed a developed a patented robotic hand that will revolutionize picking and packing. The proposed technology will create value by packing and shipping products faster while eliminating employee injuries related to repetitive tasks in stressful environments.The capability of the robot hand technology is enabled via two key advances: adaptive mechanical design and high reliability tactile sensors. The mechanics of the hand is based on specially designed flexure joints with multiple degrees of freedom that enable the fingers to passively shape themselves to the object, simplifying control, reducing the information required to grasp an object, lowering costs, and providing a gentle touch. The tactile sensing technology helps the hand adapt to object shape (especially for light objects), and detect errors. The devices are more sensitive and far easier to manufacture than competing technologies. The team will be working on developing this robotic hand technology for the product handling market. The current gold standard for handling objects is by using vacuum grippers. These grippers are simple in design and implementation, but the range of the products they can handle is quite limited. Additionally, state-of-the-art automation systems use specialized grippers, such as parallel jaw grippers, that only perform a narrow range of tasks, require experts to select the proper gripper (a costly and slow process), and must be replaced if product lines change.