A Communication-Free Human-Robot-Collaboration Approach for Aircraft Riveting Process Using AI Probabilistic Planning 2020-01-0013
In large scale industries attempts are continuously being made to automate assembly processes to not only increase productivity but also alleviate non-ergonomic tasks. However this is not always technologically possible due to specific joining challenges and the high number of special-purpose parts. For the riveting process, for example, semi-automated approaches represent an alternative to optimizing aircraft assembly and to reduce the exposure of workers to non-ergonomic conditions entailed by performing repetitive tasks.
In , a semi-automated solution is proposed for the riveting process of assembling the section barrel of the aft section to its pressure bulkhead. The method introduced a dynamic task sharing strategy between human and robot that implements interaction possibilities to establish a communication between a human and a robot in Human-Robot-collaboration fashion. Although intuitive, interacting with the robot constantly is still not natural for the worker as in the manual process no explicit communication between both workers is needed.
In this work a communication-free Human-Robot-collaboration solution is presented. The method developed not only enables sharing assembly missions by dividing tasks based on skills, but also offers the possibility of decision making to the robot. In this context, off-the-shelf Artificial Intelligence planning tools are used to model the work-flow of the human as well as the task of the robot handling alongside possible uncertainties yielding while perceiving the environment or the activity of the human.
Citation: Rekik, K., Müller, R., Hoffmann, J., and Vette, M., "A Communication-Free Human-Robot-Collaboration Approach for Aircraft Riveting Process Using AI Probabilistic Planning," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(3):1160-1167, 2020, https://doi.org/10.4271/2020-01-0013. Download Citation
Khansa Rekik, Rainer Müller, Jörg Hoffmann, Matthias Vette
ZeMA gGmbH, Saarland University
SAE International Journal of Advances and Current Practices in Mobility-V129-99EJ
Artificial intelligence (AI)
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