NRI: FND: Intelligent Co-robots for Complex Welding Manufacturing through Learning and Generalization of Welders Capabilities

高技能焊工的短缺严重且日益严重，制造复杂性和生产量不断上升的事实更是加剧了这一问题。因此，全球机器人焊接的使用正在迅速扩大。然而，在执行需要复杂技能的复杂焊接任务时，目前的焊接机器人不像人类焊工那样具有适应性和创造性。该奖项支持推进机器人能力的基础研究，以实现复杂焊接任务的完全机器人自动化。这项研究将赋予协作焊接机器人复杂的焊接知识、专家智能和交互学习能力，使它们能够应对动态的焊接场景。研究成果将增强机器人控制的科学基础，促进实现全自动化、机械化、智能化制造。这项研究涉及多个学科，包括焊接、过程监控、数据可视化、机器学习、优化和机器人控制。这种多学科方法将扩大来自不同背景的学生参与研究的范围，所获得的知识将被纳入机器人和智能制造的课程。双电极气体保护金属弧焊过程复杂，需要专家焊工密切合作。因此，如此复杂的焊接过程的机器人自动化需要在机器人感知、学习和控制的科学基础上取得进展。该项目将研究先进的方法来提取焊接领域的专家知识，并对这些知识进行量化和解释，以供协作机器人使用，从而使协作焊接机器人能够执行复杂的焊接任务。为了实现这一目标，研究小组将：1)建立身临其境的虚拟现实系统，其具有能够表征焊接场景和记录人类操作的熔池和电弧的三维渲染；2)使用可解释的递归卷积神经网络对人类焊工的火炬操纵进行因果分析，以获得其与焊接熔池/电弧动态演变的关系；3)通过使用迁移学习来从不同的人类焊工提取共同的潜在知识来概括结果，4)开发一个互动学习模块，通过基于强化学习的语言指令和人类手势感知，允许协作机器人由现场的人类焊工进行监督。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。

There is a dramatic and growing shortage of highly skilled welders, accentuated by the fact that manufacturing complexity and production volumes are rising. As a result, global use of robotic welding is expanding rapidly. However, current welding robots are not as adaptive and creative as human welders in performing complex welding tasks that require sophisticated skills. This award supports fundamental research on advancing the robotic capabilities needed to realize fully robotic automation of complex welding tasks. The research will endow collaborative welding robots with sophisticated welding knowledge, expert intelligence, and an interactive learning capability to enable them to address dynamic welding scenarios. The research results will both enhance the scientific base for robotic control and facilitate the realization of fully automatic, robotic, and intelligent manufacturing. The research involves several disciplines, including welding, process monitoring, data visualization, machine learning, optimization, and robotic control. That multi-disciplinary approach will broaden the participation of students from diverse backgrounds in research, and the knowledge gained will be incorporated in curricula in robotic and intelligent manufacturing. The double-electrode, gas metal arc welding process is complex, requiring intense collaboration between expert welders. As a result, the robotic automation of such a complex welding process requires advances in the scientific base of robotic perception, learning, and control. The project will research advanced methods for the extraction of expert welding-domain knowledge and the quantification and interpretation of that knowledge for use by collaborative robots, thereby equipping collaborative welding robots to perform complex welding tasks. To realize that goal, the research team will: 1) build an immersive virtual reality system with a three-dimensional rendering of the weld pool and arc that can characterize the weld scene and record human operations, 2) use an explainable recurrent convolutional neural network to perform causal analysis of the torch manipulation of human welders to obtain its relationship to dynamic weld pool/arc evolution, 3) generalize the results in terms of human heterogeneity by using transfer learning to extract common latent knowledge from different human welders, and 4) develop an interactive learning module that allows collaborative robots to be supervised by on-site human welders through the reinforcement learning-based perception of language instructions and human gestures.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.