Evaluation of Human-Robot Collaboration Models for Fluent Operations in Industrial Tasks
This addresses efficiency in industrial automation for workers, but it is incremental as it builds on existing collaboration models.
The study tackled the problem of improving human-robot collaboration in industrial assembly tasks by evaluating different models, and found that an adaptive model reduced total assembly time by 7% and total idle time by 60% compared to timing and sensor-based models.
In this study we evaluated human-robot collaboration models in an integrated human-robot operational system. An integrated work cell which includes a robotic arm working collaboratively with a human worker was specially designed for executing a real-time assembly task. Eighty industrial engineering students aged 22-27 participated in experiments in which timing and sensor based models were compared to an adaptive model developed within this framework. Performance measures included total assembly time and total idle time. The results showed conclusively that the adaptive system improved the examined parameters and provided an improvement of 7% in total assembly time and 60% in total idle time when compared to timing and sensory based models.