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The effect of human autonomy and robot work pace on perceived workload in human-robot collaborative assembly work
Wietse van Dijk1, Saskia J Baltrusch1, Ezra Dessers2
1Healthy Living, TNO, Leiden, Netherlands.
Frontiers in Robotics and AI
|November 29, 2023
Summary
Collaborative robots (cobots) can impact job quality. Increasing human autonomy and optimizing robot pace significantly reduce perceived workload, enhancing the worker experience in human-robot collaboration.
Area of Science:
- Human-Robot Interaction
- Industrial Engineering
- Occupational Psychology
Background:
- Collaborative robots (cobots) offer assistance in demanding tasks but can affect job quality.
- Human-robot collaboration involves task interdependence, influencing operator autonomy and work pace.
- Autonomy and work pace are critical determinants of job quality in human-robot systems.
Purpose of the Study:
- To systematically examine the impact of human autonomy and robot work pace on job quality.
- To evaluate how different collaboration modes affect perceived workload.
- To identify design factors for optimizing human-robot collaboration.
Main Methods:
- An experimental study with 20 participants in an industrial assembly task.
- Comparison of three conditions: human-led (HL), fast-paced robot-led (FRL), and slow-paced robot-led (SRL).
- Assessment of perceived workload using the NASA Task Load Index (TLX).
Main Results:
- Higher human autonomy correlated with significantly lower perceived workload.
- Slower robot pace reduced perceived workload, specifically cognitive and temporal demands.
- Human-led and robot-led conditions showed distinct impacts on workload metrics.
Conclusions:
- Increasing human autonomy is an effective strategy for improving job quality in cobot environments.
- Adjusting robot work pace, particularly reducing it, can mitigate cognitive and temporal demands.
- Design interventions focusing on autonomy and pace are crucial for successful cobot implementation.
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