Sage Journals: Discover world-class research

Abstract

The aim of this research is to develop a framework to allow efficient human robot collaboration on manufacturing assembly tasks based on cost functions that quantify capabilities and performance of each element in a system and enable their efficient evaluation. A proposed cost function format is developed along with initial development of two example cost function variables, completion time and fatigue, obtained as each worker is completing assembly tasks. The cost function format and example variables were tested with two example tasks utilizing an ABB YuMi Robot in addition to a simulated human worker under various levels of fatigue. The total costs produced clearly identified the best worker to complete each task with these costs also clearly indicating when a human worker is fatigued to a greater or lesser degree than expected.

Keywords

Dynamic task allocation robotics machine learning scheduling automation

Get full access to this article

View all access options for this article.

References

IRB 14000: YuMi – industrial robots from ABB robotics, http://new.abb.com/products/robotics/industrial-robots/yumi (accessed 27 June 2018).

Baxter Collaborative Robots for Industrial Automation | Rethink Robotics, http://www.rethinkrobotics.com/baxter/ (accessed 27 June 2018).

Sawyer Collaborative Robots for Industrial Automation | Rethink Robotics, http://www.rethinkrobotics.com/sawyer/ (accessed 27 June 2018).

Roncone A, Mangin O and Scassellati B. Transparent role assignment and task allocation in human robot collaboration. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), 29 May 2017, pp.1014–1021. New York: IEEE.

El Makrini I, Merckaert K, Lefeber D, et al. Design of a collaborative architecture for human-robot assembly tasks. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 September 2017, pp.1624–1629. New York: IEEE.

Goto H, Miura J and Sugiyama J. Human-robot collaborative assembly by on-line human action recognition based on an FSM task model. In: Human-robot interaction 2013 workshop on collaborative manipulation, 2013.

Riedelbauch D and Henrich D. Coordinating flexible human-robot teams by local world state observation. In: 2017 26th IEEE international symposium on Robot and Human Interactive Communication (RO-MAN), 28 August 2017, pp.1000–1005. New York: IEEE.

Antonelli D and Bruno G. Dynamic task sharing strategy for adaptive human-robot collaborative workcell. In: DEStech Transactions on Engineering and Technology Research, ICPR 2017.

Chen

. Optimal task allocation for human–machine collaborative manufacturing systems. IEEE Robot Autom Lett 2017; 2: 1933–1940.

10.

Johannsmeier

Haddadin

. A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes. IEEE Robot Autom Lett 2017; 2: 41–48.

11.

Digiesi

Kock

Mummolo

, et al. The effect of dynamic worker behavior on flow line performance. Int J Prod Econ 2009; 120: 368–377.

12.

Dawson

Noy

Härmä

, et al. Modelling fatigue and the use of fatigue models in work settings. Accident Anal Prev 2011; 43: 549–564.

13.

https://www.thingiverse.com/thing:1460364 (accessed 29 November 2018).

14.

Stolt A, Carlson FB, Ardakani MM, et al. Sensorless friction-compensated passive lead-through programming for industrial robots. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 28 September 2015, pp.3530–3537. New York: IEEE.

15.

Ding H, Schipper M and Matthias B. Optimized task distribution for industrial assembly in mixed human-robot environments – case study on IO module assembly. In: 2014 IEEE International Conference on Automation Science and Engineering (CASE), 18 August 2014, pp.19–24. New York: IEEE.

16.

Berkeley AutoLab yumipy documentation – yumipy 0.0.1 documentation, https://berkeleyautomation.github.io/yumipy/ (accessed 28 June 2018).

Assessing worker performance using dynamic cost functions in human robot collaborative tasks

Abstract

Keywords

Get full access to this article

References