Manufacturing challenges are driving the move from separated workspaces of either humans or robots towards a close, symbiotic collaboration. Symbiotic Human-Robot Collaboration requires both parties to not only share the same workspace, but to also perform tasks simultaneously. This raises questions of mutual awareness, for which safety is a critical factor. Despite advances regarding safety systems, human sensing abilities combined with the intelligence to anticipate potential emergencies cannot be matched. Subsequently, the human operator remains in a critical role regarding safety in Human-Robot Collaboration However, in a collaborative environment humans are expected to use their hands towards the completion of a task. Therefore, in order to achieve resilience for collaborative tasks, there is a need to have a hands free detection mechanism for unforeseen events. This work investigates a human sensor-based emergency stop interface that reacts once the human operator senses or anticipates a potential emergency. A novel approach is presented on how a mobile electroencephalogram (EEG) can be used to detect potential emergencies in Human-Robot Collaboration. An experiment was conducted with 21 participants, ten assembly tasks and three different kinds of potential emergencies. The potential emergencies included the collaborative robot to drop an assembly workpiece, to crush the assembly piece on the worktable, and to perform a simulated malfunction. The EEG data suggests strong similarities in the patterns between the different types of potential emergencies. High accuracies were be achieved with a Decision Tree Model based on Continuous Wavelet Transform peak counting. To optimize detection time, different detection window sizes were compared. The results showed a promising potential of this approach, which it is not intended to replace current safety systems but to enhance them towards a safer and thus symbiotic Collaboration.

制造方面的挑战正在推动人类或机器人从分离的工作空间向紧密，共生的协作发展。共生人机协作需要双方不仅共享同一工作空间，而且还必须同时执行任务。这就提出了相互意识的问题，对此安全是至关重要的因素。尽管在安全系统方面取得了进步，但人类的感知能力与智能相结合以预测潜在的紧急情况仍无法实现。随后，操作员在人机协作的安全性方面仍然扮演着至关重要的角色。但是，在协作环境中，人们期望人们用他们的双手来完成任务。因此，为了获得协作任务的弹性，对于不可预见的事件，需要有一种免提检测机制。这项工作研究了基于人类传感器的紧急停止界面，一旦操作员感觉到或预期到潜在的紧急情况，该界面就会做出反应。提出了一种新颖的方法，说明如何使用移动脑电图（EEG）来检测人机协作中的潜在紧急情况。进行了一个实验，有21名参与者，10个装配任务和三种不同类型的紧急事件发生。潜在的紧急情况包括协作机器人将装配工件掉落，将装配件压在工作台上并执行模拟故障。EEG数据表明，不同类型的潜在紧急事件之间的模式具有很大的相似性。使用基于连续小波变换峰值计数的决策树模型可以实现较高的准确性。为了优化检测时间，比较了不同的检测窗口大小。结果表明，这种方法具有广阔的发展前景，它并不是要取代当前的安全系统，而是要增强它们的安全性，从而实现共生协作。