Data-based Iterative Control using Complex-Kernel Regression for Precision SEA Robots

这笔赠款将支持研究，这些研究将在高需求、低产量的制造部门，如航空航天，贡献与增加自动化相关的新知识。与完全自动化相比，需要对小批量制造的半自主方法进行增长趋同研究，这种方法利用人类适应性与机器精度和速度的结合，以实现成本效益。具有串联弹性驱动器(SEA)的机器人具有软关节，这使得能够精确地控制施加在环境中的力，因此被认为对于人与机器人的协作具有内在的安全性。这种固有的安全性便于工人采用，他们可以通过物理演示直接对机器人进行编程，这反过来又减少了新工人所需的培训量。然而，增加对力的控制是以较低的定位精度为代价的，这限制了它们在制造业中的使用，因为制造业对精度很重要。这项研究的结果将提高这种本质安全的机器人的精度，并使相对新手能够使用它们。此外，使用机器人解决方案在有限的空间内制造，而不是人类在里面爬行，可以带来更薄、更轻、更高效的飞机机翼，而运营成本更低。因此，这项工作将直接影响美国在航空航天制造业的竞争力，提供大量高薪工作。本研究涉及控制理论与先进机器人技术在制造业中的整合。由于人们对制造和机器人的兴趣与日俱增，这些努力将有助于增加未被充分代表的群体参与研究，并加强工程教育。相对柔软的串联弹性致动器以及低阻抗控制提高了对这类机器人对环境施加的力的控制权威，并有可能在制造环境中实现人与机器人的协作。然而，一个中心问题是，这类机器人中的弯曲系统会导致非最小相位动力学，高增益(为了提高精度)可能会导致不稳定。此外，由于串联弹性致动器机器人中存在大量的摩擦非线性、间隙和接触相关效应，为提高精度而进行精确建模可能是具有挑战性的。这项研究将填补基于数据的迭代机器学习方法的知识空白，以提高此类系统的精度。该研究将使用基于核的学习方法的不确定性估计来开发保证收敛的迭代增益大小的条件。该方法将在有限空间制造试验台上进行试验性评估。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。

This grant will support research that will contribute new knowledge related to increased automation in high-demand, low-volume manufacturing sectors, such as aerospace. In contrast to full automation, there is a need for growing-convergence research on semi-autonomous approaches for low-volume manufacturing, which exploit the combination of human adaptability and machine precision and speed, to be cost effective. Robots with series-elastic actuators (SEA) have soft joints, which enables precision control over the forces applied to the environment and are therefore, considered to be inherently safe for human-robot collaboration. This inherent safety facilitates easy adoption by workers who can directly program the robots by physical demonstrations, which in turn reduces the amount of training needed for new workers. Nevertheless, this increased control over forces comes at the cost of lower positioning precision, which limits their use in manufacturing, where precision is important. The results from this research will increase the precision of such inherently-safe robots, and enable their use by relatively-novice workers. Moreover, the use of robotic solutions for manufacturing in confined spaces, rather than a human crawling inside, can lead to thinner, lighter and more efficient aircraft wings, with lower operating costs. Thus, the work will directly impact US competitiveness in the aerospace manufacturing sector with a substantial number of high-paying jobs. This research involves the integration of control theory and advanced robotics in manufacturing. Due to substantial and growing interest in manufacturing and robotics, the efforts will help to increase participation by underrepresented groups in research, and strengthen engineering education.Relatively-soft, series elastic actuators along with low-impedance control improves control authority over the force exerted by such robots on the environment, and has the potential to enable human-robot collaboration in the manufacturing environment. Nevertheless, a central issue is that the flexural systems in such robots result in non-minimum phase dynamics and high gains (for improved precision) can lead to instability. Moreover, accurate modeling for increased precision can be challenging due to substantial friction nonlinearities, backlash, and contact-related effects in series elastic actuators robots. This research will fill the knowledge-gap on data-based iterative machine learning approaches to improve the precision of such systems. The research will use uncertainty estimates from the kernel-based learning approach to develop conditions on the size of the iteration gain for guaranteed convergence. The approach will be experimentally evaluated with a confined-space manufacturing testbed.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.