Recent emergence of safe, lightweight, and flexible robots has opened a new realm for human-robot collaboration in manufacturing. Utilizing such robots with the new human-robot interaction (HRI) functionality to interact closely and effectively with a human co-worker, we propose a novel framework for integrating HRI factors (both physical and social interactions) into the robot motion controller for human-robot collaborative assembly tasks in a manufacturing hybrid cell. To meet human physical demands in such assembly tasks, an optimal control problem is formulated for physical HRI (pHRI)-based robot motion control to keep pace with human motion progress. We further augment social HRI (sHRI) into the framework by considering a computational model of the human worker's trust in his/her robot partner as well as robot facial expressions. The human worker's trust in robot is computed and used as a metric for path selection as well as a constraint in the optimal control problem. Robot facial expression is displayed for providing additional visual feedbacks to the human worker. We evaluate the proposed framework by designing a robotic experimental testbed and conducting a comprehensive study with a human-in-the-loop. Results of this paper show that compared to the manual adjustments of robot velocity, an autonomous controller based on pHRI, pHRI and sHRI with trust, or pHRI and sHRI with trust, and emotion result in 34%, 39%, and 44% decrease in human workload and 21%, 32%, and 60% increase in robot's usability, respectively. Compared to the manual framework, human trust in robot increases by 38% and 42%, respectively, in the latter two autonomous frameworks. Moreover, the overall efficiency in terms of assembly time remains the same.

中文翻译：

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混合制造单元中的协作组装：人类机器人交互的集成框架


近年来安全、轻便、灵活的机器人的出现，为制造领域的人机协作开辟了新的领域。利用具有新的人机交互（HRI）功能的机器人与人类同事进行密切有效的交互，我们提出了一种新颖的框架，用于将 HRI 因素（物理和社交交互）集成到机器人运动控制器中，以实现人类制造混合单元中的机器人协作装配任务。为了满足此类装配任务中的人类物理需求，为基于物理 HRI (pHRI) 的机器人运动控制制定了最优控制问题，以跟上人类运动进展。我们通过考虑人类工人对其机器人伙伴的信任以及机器人面部表情的计算模型，进一步将社交 HRI (sHRI) 增强到框架中。计算人类工人对机器人的信任度并将其用作路径选择的度量以及最优控制问题的约束。显示机器人面部表情，为人类工人提供额外的视觉反馈。我们通过设计机器人实验测试台并通过人机交互进行全面研究来评估所提出的框架。本文的结果表明，与手动调节机器人速度相比，基于 pHRI、pHRI 和 sHRI 信任，或者 pHRI 和 sHRI 信任和情感的自主控制器导致机器人速度分别降低了 34%、39% 和 44%。人类工作量和机器人可用性分别增加 21%、32% 和 60%。与手动框架相比，在后两个自主框架中，人类对机器人的信任度分别增加了 38% 和 42%。此外，装配时间方面的整体效率保持不变。