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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Using Modeling and Simulation to Predict Operator Performance and Automation-Induced Complacency With Robotic
Christopher D Wickens1, Angelia Sebok2, Huiyang Li3
1Alion Science and Technology, Boulder, Colorado cwickens@alionscience.com.
Human Factors
|April 8, 2015
Summary
This study developed a computational model to predict automation complacency in robotic arm tasks. The model accurately predicted operator responses to automation failures after complacency developed, highlighting the need to support situation awareness.
Area of Science:
- Human-computer interaction
- Cognitive modeling
- Automation systems
Background:
- Existing computational models of automation complacency are limited to simplified monitoring failures.
- This research extends model validation to complex robotic arm tasks.
Purpose of the Study:
- To develop and validate a computational model of automation complacency.
- To assess the impact of different automation degrees on operator performance.
- To inform system designers about automation trade-offs without extensive human-in-the-loop testing.
Main Methods:
- A realistic simulation of a space-based robotic arm task was created with three automation levels.
- Human-in-the-loop testing was conducted to induce and assess complacency.
- A multicomponent model of the robotic operator was developed based on cognitive task analysis and visual scanning.
Main Results:
- The computational model accurately predicted routine performance and responses to automation failures post-complacency.
- The model's visual scanning component did not fully capture all attention allocation effects of complacency.
Conclusions:
- Computational modeling of automation complacency is valuable for predicting the impact of imperfect automation.
- Future automation development should prioritize enhancing operator situation awareness.
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