The collaborative robot market is flourishing as there is a trend towards simplification, modularity, and increased flexibility on the production line. But when humans and robots are collaborating in a shared environment, the safety of humans should be a priority. We introduce a novel wearable robotic system to enhance safety during Human-Robot Interaction (HRI). The proposed wearable robot is designed to hold a fiducial marker and maintain its visibility to a motion capture system, which, in turn, localizes the user's hand with good accuracy and low latency and provides vibrotactile feedback to the user's wrist. The vibrotactile feedback guides the user's hand movement during collaborative tasks in order to increase safety and enhance collaboration efficiency. A user study was conducted to assess the recognition and discriminability of ten designed vibration patterns applied to the upper (dorsal) and the down (volar) parts of the user's wrist. The results show that the pattern recognition rate on the volar side was higher, with an average of 75.64% among all users. Four patterns with a high recognition rate were chosen to be incorporated into our system. A second experiment was carried out to evaluate users' response to the chosen patterns in real-world collaborative tasks. Results show that all participants responded to the patterns correctly, and the average response time for the patterns was between 0.24 and 2.41 seconds.

翻译：随着生产线向简化、模块化和灵活性增强的趋势发展，协作机器人市场正在蓬勃发展。但当人类与机器人在共享环境中协作时，人类的安全应成为首要考量。本文介绍了一种新颖的可穿戴机器人系统，旨在提升人机交互（HRI）过程中的安全性。所提出的可穿戴机器人设计用于固定基准标记并保持其对运动捕捉系统的可见性，该系统进而能以高精度和低延迟对用户手部进行定位，并向用户手腕提供振动触觉反馈。该振动触觉反馈在协作任务中引导用户手部运动，以提高安全性并增强协作效率。我们进行了一项用户研究，以评估施加于用户手腕上部（背侧）和下部（掌侧）的十种设计振动模式的识别率与可区分性。结果表明，掌侧的模式识别率更高，所有用户的平均识别率为75.64%。我们选取了四种高识别率的模式集成到我们的系统中。第二项实验评估了用户在现实协作任务中对所选模式的响应情况。结果显示，所有参与者均能正确响应这些模式，且对模式的平均响应时间在0.24至2.41秒之间。