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Updated: Mar 11, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
Published on: August 29, 2025
Dynamic velocity scaling for industrial collaborative robots: a gaze-driven approach.
Matteo Manzardo1, Federico Fraboni2, Sofia Morandini2
1Faculty of Engineering, Free University of Bozen/Bolzano, 39100, Bolzano, Italy.
This study introduces a novel human-robot seamless interaction (HRSI) method that enhances productivity and safety by dynamically adjusting robot behavior based on operator visual attention and cognitive workload. The adaptable robotic system significantly improves collaboration effectiveness.
Area of Science:
- Robotics
- Human-Computer Interaction
- Cognitive Science
Background:
- Effective human-robot seamless interaction (HRSI) is crucial for industrial robotics, requiring integration of human mental processes into robot actions.
- Existing research often overlooks high-frequency cognitive processes like visual attention, focusing primarily on low-frequency processes such as cognitive workload.
- Optimizing safety, productivity, and ergonomics in human-robot collaboration necessitates a comprehensive approach to human cognitive states.
Purpose of the Study:
- To develop and validate a real-time method for adjusting robot behavior based on both high-frequency (visual attention) and low-frequency (cognitive workload) human cognitive processes.
- To investigate the impact of dynamic manipulator speed modulation based on operator visual attention.
- To assess the influence of robot trajectory adjustments for optimizing operator cognitive workload.
Main Methods:
- A system was developed to monitor operator gaze for assessing visual attention, dynamically modulating manipulator speed accordingly.
- Robot trajectories were adjusted in real-time to optimize the operator's cognitive workload.
- An experimental validation was conducted with 26 participants to evaluate the system's performance.
Main Results:
- The developed algorithm resulted in an 18% improvement in productivity, 5% reduction in cognitive workload, 10% increase in fluency, and 5-9% improvements in usability, reliability, and acceptance.
- Exploiting high-frequency cognitive processes (visual attention) significantly improved all measured metrics.
- Increased adaptability of the robotic system positively correlated with enhanced collaboration effectiveness.
Conclusions:
- The proposed method effectively integrates high-frequency and low-frequency cognitive processes for improved human-robot seamless interaction (HRSI).
- Real-time adaptation based on operator visual attention and cognitive workload is vital for optimizing industrial robotic systems.
- Future research in HRSI should prioritize the exploitation of high-frequency cognitive processes for enhanced human-robot collaboration.
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