Integrating intelligence and knowledge of human factors to facilitate collaboration in manufacturing
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Abstract
The implementation of automation has become a common
occurrence in recent years, and automated robotic systems are
actively used in many manufacturing processes. However, fully
automated manufacturing systems are far less common, and
human operators remain prevalent. The resulting scenario is one
where human and robotic operators work in close proximity, and
directly affect the behavior of one another. Conversely to their
robotic counterparts, human beings do not share the same level
of repeatability or accuracy, and as such can be a source of
uncertainty in such processes.
Concurrently, the emergence of intelligent manufacturing
has presented opportunities for adaptability within robotic
control. This work examines relevant human factors and
develops a learning model to examine how to utilize this
knowledge and provide appropriate adaptability to robotic
elements, with the intention of improving collaborative
interaction with human colleagues, and optimized performance.
The work is supported by an example case-study, which explores
the application of such a control system, and its performance in
a real-world production scenario