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产品的仓储企业，这些产品具有各种形状和尺寸。这些仓库为消费电子、食品配送和制药等领域的在线企业提供存储和履行物流。例子包括Amazon、Target和CVS。大约有172 000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运输订单需求，这些需求可能会受到季节性假期，时尚趋势甚至是一周中的某一天的影响。这些仓库目前的解决方案是雇用额外的员工来挑选和包装这些包裹时，需求的增长。自动化这些订单拣选任务还可以解决季节性劳动力波动和恶劣工作条件的问题。 这个研究小组已经开发出一种专利机器人手，它将彻底改变采摘和包装。 该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性任务而受伤的情况。机器人手技术的能力通过两个关键进步实现：自适应机械设计和高可靠性触觉传感器。手的力学是基于专门设计的具有多个自由度的柔性关节，使手指能够被动地根据物体塑造自己，简化控制，减少抓取物体所需的信息，降低成本，并提供柔和的触摸。触觉传感技术可以帮助手适应物体形状（特别是轻物体），并检测错误。这些设备比竞争技术更敏感，也更容易制造。该团队将致力于为产品处理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抓手。这些夹持器在设计和实施上都很简单，但是它们可以处理的产品范围非常有限。此外，最先进的自动化系统使用专门的夹持器，例如平行钳口夹持器，这些夹持器仅执行范围很窄的任务，需要专家选择合适的夹持器（成本高且缓慢的过程），并且如果生产线发生变化，则必须更换。

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.