SBIR Phase II: Gripper-integrated proximity, contact and force sensing for collaborative robots

SBIR 第二阶段：协作机器人的夹具集成接近、接触和力传感

• 批准号: 1853146
• 负责人: Nicolaus Correll
• 金额: $ 74.31万
• 依托单位: Robotic Materials Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1853146&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Gripper; integrated

The broader impact/commercial potential of this project is a new generation of smart robotic hands that will manifest a new plateau for industrial manipulation and robotics research. Previously hard manipulation challenges will be available out-of-the-box, enabling industry to create new, complex applications. Integration of sensing, actuation, control, and programming environment into a single low-cost device will decrease complexity, setup time and cost of collaborative robotic solutions, and, together with a new generation of low-cost robotic arms, enable industrial tasks of high commercial value, including bin-picking, kitting and assembly. A simple, graphical programming interface paired with a browser-based scripting interface will make robot programming accessible to a workforce with widely varying educational backgrounds, thereby increasing productivity and efficiency in the workplace and ensuring long-term economic viability of US-based manufacturing. This project also contributes to workforce development by training a new breed of engineers that are equally versed with mechanical, electronics and control, while providing opportunities for a diverse population of students at the University of Colorado. This Small Business Innovation Research Phase II project fundamentally advances the understanding of the interplay between proximity, contact and force sensing; self-supervised learning techniques to improve hand-coded robot controllers; and manufacturing challenges of systems that tightly integrate sensing, actuation, and computation. Building up on a proof-of-concept gripper design resulting from Phase I research that has successfully demonstrated a variety of challenging industrially-relevant bin picking, kitting and assembly tasks, this research will investigate new algorithms for dual-arm assembly problems including improved path planning and collision avoidance, optimization algorithms for automatic parameter tuning to improve grasp and assembly success, and training of deep convolutional neural networks to replace hand-coded segmentation algorithms for object identification and ?actor critics?. Rigorous testing for robustness, reliability and accuracy throughout the project will drive algorithm development. Safety analysis will feed back into hardware and controller design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目更广泛的影响/商业潜力是新一代智能机器人手，它将展示工业操纵和机器人研究的新平台。以前的硬操作挑战将开箱即用，使行业能够创建新的复杂应用程序。将传感、驱动、控制和编程环境集成到单个低成本设备中，将降低协作机器人解决方案的复杂性、安装时间和成本，并与新一代低成本机械臂一起，实现具有高商业价值的工业任务，包括拾取垃圾箱、套件和组装。简单的图形编程界面与基于浏览器的脚本界面相结合，将使不同教育背景的劳动力能够进行机器人编程，从而提高工作场所的生产率和效率，并确保美国制造业的长期经济可行性。该项目还通过培养同样精通机械、电子和控制的新型工程师来促进劳动力发展，同时为科罗拉多大学的不同群体的学生提供机会。这个小型企业创新研究第二阶段项目从根本上提高了对接近、接触和力传感之间的相互作用的理解；改进手工编码机器人控制器的自我监督学习技术；以及紧密集成传感、驱动和计算的系统的制造挑战。这项研究建立在第一阶段研究产生的概念验证夹持器设计的基础上，该设计成功地展示了各种具有挑战性的工业相关拾取、装配和装配任务，本研究将研究用于双臂装配问题的新算法，包括改进的路径规划和碰撞避免，用于自动参数调整的优化算法以提高抓取和装配成功率，以及训练深度卷积神经网络以取代用于对象识别和演员批评的手动编码分割算法。在整个项目中，对健壮性、可靠性和准确性的严格测试将推动算法的开发。安全分析将反馈到硬件和控制器设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。