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

这笔拨款将支持研究，这些研究将为高需求、小批量制造部门（如航空航天）增加自动化提供新知识。与完全自动化相比，需要对小批量制造的半自动方法进行日益收敛的研究，这种方法利用人的适应性与机器精度和速度的结合，以达到成本效益。具有串联弹性致动器（SEA）的机器人具有柔软的关节，可以精确控制施加到环境中的力，因此被认为是人机协作的本质安全。这种固有的安全性使工人可以通过物理演示直接对机器人进行编程，从而减少了新工人所需的培训量。然而，这种对力的控制是以较低的定位精度为代价的，这限制了它们在精度很重要的制造业中的使用。这项研究的结果将提高这种天生安全的机器人的精度，并使它们能够被相对新手的工人使用。此外，使用机器人解决方案在密闭空间进行制造，而不是在里面爬行的人，可以带来更薄、更轻、更高效的飞机机翼，同时降低运营成本。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，并提供大量高薪工作。本研究涉及控制理论与先进机器人技术在制造业中的结合。由于对制造业和机器人技术的兴趣日益浓厚，这些努力将有助于增加代表性不足的群体对研究的参与，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此类机器人对环境施加的力的控制权限，并有可能在制造环境中实现人机协作。然而，一个核心问题是，这种机器人中的弯曲系统导致非最小相位动力学和高增益（为了提高精度）可能导致不稳定。此外，由于在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具有挑战性。本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。该方法将在一个有限空间制造试验台上进行实验评估。该奖项反映了美国国家科学基金会的法定使命，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。

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.