We propose an innovative approach to enhance welding operations by using a cyber-physical system (CPS) with layered architecture and enabling a robot to be effectively operated by its commanding human. This article focuses on the recognition of the commanding human's intention that should be executed by the robot. To this end, a virtual reality (VR) system based on the HTC Vive is used to create a remote virtual welding environment. Human hand movement speed data is collected and used to train a hidden Markov model (HMM) using the Baum-Welch algorithm. The Bayesian information criterion (BIC) is applied to determine the number of hidden states. The state occupancy probability distribution (the probability of each state at a given time) is estimated based on the human hand movement speed sequence using the forward algorithm. The human intention, defined as the intended movement in this article, is then estimated as the statistical expectation of the observable variables. Using the proposed human intention estimation algorithm, the intended movement recognized from the raw movement data is smoother, which is preferable in welding tasks. A 6-DoF industrial robot, UR-5 with a custom gas tungsten arc welding (GTAW) torch installed, works as the final performer of the welding jobs. The robot receives the intended movement data from the HMM and uses this to assist the human welding operators. Welding experiments have been conducted both with and without the proposed human intention recognition (IR) algorithm. The results show that the robot can help the operators complete welding tasks with better performance using the proposed IR system, supporting the effectiveness of the proposed VR robot-assisted welding system. Note to Practitioners-Welding is not only labor-intensive but also requires real-time adaption to the process, which is challenging for robots/machines but relatively straightforward for humans. Using a human commanded robot to perform welding can liberate humans from laborious operations and hazardous environments. To this end, a virtual reality (VR) system is used to create a virtual welding environment for a human to view the process remotely and for a human to pass his/her resultant adaptation to the robot through hand movements. However, hand movements do not always fully represent the intended adaptation of the commanding human, and the recognition of such human intention is fundamental in such a proposed method. This article established the mathematical framework for the recognition of the human intention and, thus, the foundation for effective assistance of robots to humans.

中文翻译：

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基于人类意图识别的虚拟现实机器人辅助焊接


我们提出了一种创新方法，通过使用具有分层架构的网络物理系统（CPS）来增强焊接操作，并使机器人能够由其指挥人员有效操作。本文重点关注机器人应执行的指挥人类意图的识别。为此，使用基于HTC Vive的虚拟现实（VR）系统来创建远程虚拟焊接环境。收集人手运动速度数据并使用 Baum-Welch 算法训练隐马尔可夫模型 (HMM)。应用贝叶斯信息准则（BIC）来确定隐藏状态的数量。使用前向算法根据人手运动速度序列估计状态占用概率分布（给定时间每个状态的概率）。然后，将人类意图（在本文中定义为预期的运动）估计为可观察变量的统计期望。使用所提出的人类意图估计算法，从原始运动数据中识别出的预期运动更加平滑，这在焊接任务中是优选的。安装了定制钨极氩弧焊 (GTAW) 焊枪的 6 自由度工业机器人 UR-5，充当焊接作业的最终执行者。机器人从 HMM 接收预期的运动数据，并利用该数据来协助人类焊接操作员。在使用和不使用所提出的人类意图识别（IR）算法的情况下都进行了焊接实验。结果表明，机器人可以使用所提出的IR系统帮助操作员以更好的性能完成焊接任务，支持所提出的VR机器人辅助焊接系统的有效性。 从业者须知——焊接不仅是劳动密集型工作，而且需要实时适应过程，这对机器人/机器来说具有挑战性，但对人类来说相对简单。使用人类指挥的机器人进行焊接可以将人类从繁重的操作和危险的环境中解放出来。为此，虚拟现实（VR）系统用于创建虚拟焊接环境，以便人类远程查看过程，并通过手动动作将其产生的适应结果传递给机器人。然而，手部动作并不总是完全代表指挥者的预期适应，并且对这种人类意图的识别是这种所提出的方法的基础。本文建立了识别人类意图的数学框架，从而为机器人有效帮助人类奠定了基础。