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Improving human-robot-interaction utilizing learning and intelligence, a human factors based approach

Oliff, H, Liu, Y, Kumar, M and Williams, M 2019. Improving human-robot-interaction utilizing learning and intelligence, a human factors based approach. IEEE Transactions on Automation Science and Engineering 10.1109/TASE.2020.2967093

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Abstract

Several decades of development in the fields of robotics and automation have resulted in human-robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Business (Including Economics)
Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1545-5955
Date of First Compliant Deposit: 26 November 2019
Date of Acceptance: 25 November 2019
Last Modified: 29 Jun 2020 15:06
URI: http://orca.cf.ac.uk/id/eprint/127154

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