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

该项目更广泛的影响/商业潜力是新一代智能机器人手，将为工业操作和机器人研究开辟新的平台。以前难以操作的挑战将开箱即用，使行业能够创建新的复杂应用。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、设置时间和成本，并与新一代低成本机器人手臂一起，实现具有高商业价值的工业任务，包括装箱、装配和组装。一个简单的图形化编程界面与基于浏览器的脚本界面相结合，将使具有广泛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力和效率，并确保美国制造业的长期经济可行性。该项目还通过培训同样精通机械，电子和控制的新一代工程师来促进劳动力发展，同时为科罗拉多大学的多样化学生提供机会。这个小型企业创新研究第二阶段项目从根本上推进了对接近，接触和力感之间相互作用的理解;自我监督学习技术，以改善手动编码的机器人控制器;以及紧密集成传感，驱动和计算的系统的制造挑战。基于第一阶段研究的概念验证夹持器设计，该设计已成功展示了各种具有挑战性的工业相关的箱子拾取，配套和装配任务，本研究将研究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，和训练深度卷积神经网络，以取代手工编码的分割算法，用于对象识别和？演员评论家？在整个项目中对鲁棒性、可靠性和准确性进行严格的测试将推动算法的开发。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.