SBIR Phase I: Haptic Robotics for Kitting Powertrain Components

这个小型企业创新研究(SBIR)第一阶段项目提出，触觉机器人为当前需要人工完成的常见任务--拣箱和装具--提供有效和灵活的解决方案。一个流行的应用领域是对工业机器人进行编程，使其与特定的动力总成部件一起工作。问题：拾取垃圾箱的机器人只能在结构化或半结构化配置中操作具有简单几何形状的坚固部件；机器视觉处理重叠对象和不稳定和不可预测的负载或夹具的能力很差。机会：触觉系统可以与机器视觉系统相辅相成，使用触觉传感器数据来处理重叠的物体和不稳定的握力条件。解决方案是：一只装有独特仿生触觉传感器的机器人手，为开发握力算法提供了一个平台，用于部分踢球。创新包括开发滑移检测和力锥握力调整算法；开发使用触觉反馈改进抓握预成形的自适应算法；使用物理动力总成部件验证算法；以及挑战具有真实世界不确定性的算法，这些不确定性很难使用机器视觉检测。该项目的更广泛影响/商业潜力将导致机器人具有更像人类的触觉能力。目前，机器人缺乏触觉感知，依靠专门的部件送料器和抓取工具来处理具有预定抓取特征的单个对象。这些物品增加了成本、占用了空间、耗费了时间，特别是当必须处理许多不同的对象时。这使得机器人与人类工人相比处于明显的劣势，人类工人用手灵活地处理无限种类的物体。然而，机器人最适合处理对人类高度重复或危险的任务。当机器人处理危险材料或制造任务时，它们减轻了人类的这些负担，同时往往提高了生产率和节省了成本。在第一阶段结束时，我们将开发一个概念证明，以便我们的第二阶段研究可以将系统集成到工业环境中。通过解决在物体姿势、位置和零件之间的交互中存在错误的物体拾取问题，触觉机器人将改变制造过程中的这一重要部分。这种技术将对制造业和自动化的许多方面产生广泛影响。

This Small Business Innovation Research (SBIR) Phase I project proposes that haptic robots offer effective andattractive solutions to the common tasks of Bin-Picking and Kitting, which currently require human workers. Apopular application area is programming an industrial robot to work with specific powertrain parts. TheProblem: Bin-picking robots can only manipulate strong parts with simple geometries in structured or semistructuredconfigurations; machine vision deals poorly with overlapping objects and unstable and unpredictableloads or grips. The Opportunity: Haptic systems can compliment machine vision systems using tactile sensorydata to cope with overlapping objects and unstable grip conditions. The Solution: A robotic hand/armequipped with unique biomimetic tactile sensors to make a platform for developing grip algorithms for partspicking and kitting. Innovations include develop algorithms for slip-detection and force-cone grip adjustment;developing adaptive algorithms using tactile feedback to improve grip pre-shaping; validating algorithms withphysical powertrain parts; and challenging algorithms with real-world uncertainties that are difficult to detectusing machine vision.The broader impact/commercial potential of this project will result in robots that have more humanlike hapticcapabilities. Currently robots lack tactile sensing and rely on specialized component feeders and gripping toolsto handle individual objects with predetermined gripping features. These items add cost, occupy space, andconsume time, especially when many different objects must be handled. This puts robots at a significantdisadvantage to human workers who use their hands dexterously to handle an unlimited variety of objects.Nevertheless, robots are best suited to handle tasks that are highly repetitive or dangerous to humans. Whenrobots handle hazardous materials or manufacturing tasks, they relieve humans of these burdens while oftenyielding increased productivity and cost-savings. At the end of Phase I we will have developed a proof ofconcept so that our Phase II research can integrate the system into an industrial environment. By addressing thepicking of objects with errors in object pose, position and part-to-part interaction, haptic robots will transformthis important part of the manufacturing process. Such technology will have widespread impact on manyaspects of manufacturing and automation.