Abstract
Collaborative robots (cobots) represent a transformative advancement in industrial automation, designed to work alongside humans without the strict separation and safety barriers required by traditional industrial robotic systems. Their ability to operate in close proximity to human operators creates new opportunities for efficiency, flexibility, and ergonomics. However, their integration into workplaces also raises critical challenges related to human trust, perceived safety, and stress. Operators’ acceptance of cobots is closely linked to how secure and comfortable they feel during interaction, making the study of psychological and physiological responses essential for safe and effective deployment.
This research investigates how task-related factors—including cobot movement speed, task complexity and the presence of multimodal feedback (visual and auditory cues)—affect operator stress. We employ both objective physiological measures and subjective self-reports to identify conditions that minimize stress and enhance operator confidence.
The study uses a mixed-methods experimental design. Physiological metrics based on heart rate variability (HRV) are collected during collaborative tasks with the cobots. In parallel, participants provide subjective feedback through questionnaires assessing perceived safety, stress, and comfort. The experimental setup involves controlled manipulation of task speed and complexity, and the addition of visual and auditory interfaces. Quantitative data are analyzed statistically to identify significant differences across conditions, while qualitative insights help interpret the experiential dimensions of human–cobot interaction.
Initial results suggest that multimodal external stimuli play a protective role: both auditory and visual cues reduce stress compared to no feedback, and their combination yields the most significant effect. Conversely, direct physical contact with cobots increases physiological stress markers and reduces perceived safety, emphasizing the importance of designing cobot tasks to minimize unnecessary contact.
The findings highlight design considerations for next-generation cobots, including the integration of multimodal feedback systems, careful calibration of movement speed, and strategies to limit physical interaction. These insights can guide safer cobot deployment, improve user training programs, and foster greater trust between humans and robots. Future work will extend this research to explore the influence of user-related variables such as age, gender, prior experience with automation, and task type. The broader aim is to develop design and training guidelines that can be tailored to diverse user populations, ultimately contributing to safer and more inclusive human–robot collaboration.